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Dynamic Evolution of Copper-Based Catalysts in Electrochemical Nitrate Reduction to Ammonia

Yabo Wang , Gangfeng Du , Zhengshan Tian , Zihong Pan , Kesheng Cao , Haoqi Wang

Progress in Chemistry ›› 2026, Vol. 38 ›› Issue (5) : 809 -831.

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Progress in Chemistry ›› 2026, Vol. 38 ›› Issue (5) : 809-831 DOI: 10.7536/PC20250813
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Dynamic Evolution of Copper-Based Catalysts in Electrochemical Nitrate Reduction to Ammonia
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Abstract

The dynamic evolution of catalysts is a crucial phenomenon in electrocatalysis， particularly in the nitrate electroreduction to ammonia （NO3RR） process. This review systematically studies the mechanisms underlying the dynamic evolution of copper-based catalysts during NO3RR， emphasizing how reconstructed structures can significantly influence electrochemical performance. Achieving a designed active surface through dynamic evolution is essential for optimizing catalytic efficiency. We highlight advanced electrochemical， microscopic， and spectroscopic techniques that are instrumental in tracking these dynamic processes， providing insights into how structural changes occur in real-time. Moreover， we present a comprehensive summary of the latest strategies for regulating dynamic evolution， including valence-state control， morphological engineering， crystal facet optimization， heterogeneous interface construction， and in-situ defect engineering. These approaches effectively harness the dynamic nature of catalysts to enhance their performance in NO3RR. However， several challenges remain， such as the mechanistic ambiguity surrounding active sites， limited capabilities for in-situ monitoring， trade-offs between stability and activity， and scalability barriers. This review concludes by offering perspectives for future research， asserting that controlled dynamic evolution is pivotal for unlocking the full potential of Cu-based catalysts in the pursuit of sustainable nitrate reduction to ammonia.

Contents

1 Introduction

2 Mechanism of evolution

3 Characterizations of dynamic evolution

3.1 Electrochemical characterizations

3.2 Microstructure characterizations

3.3 Spectroscopic characterizations

4 Regulation strategies for copper dynamics

4.1 Valence-state control

4.2 Morphological engineering

4.3 Crystal facet optimization

4.4 Heterogeneous interface construction

4.5 Defect engineering

5 Challenges and perspectives

6 Conclusions

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Keywords

dynamic evolution / nitrate reduction / electrocatalysis / copper catalysts

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Yabo Wang, Gangfeng Du, Zhengshan Tian, Zihong Pan, Kesheng Cao, Haoqi Wang. Dynamic Evolution of Copper-Based Catalysts in Electrochemical Nitrate Reduction to Ammonia. Progress in Chemistry, 2026, 38(5): 809-831 DOI:10.7536/PC20250813

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1 Introduction

Electrochemical nitrate reduction to ammonia （NO3RR） offers an attractive route to convert nitrate pollutants and nitrogen feedstocks into a valuable chemical under mild conditions. Copper-based materials have emerged as promising catalysts because of their favorable binding energetics for nitrogen-containing intermediates and relatively low cost [1]. However， despite numerous studies reporting high initial activities or selectivity for Cu catalysts， many results show time-dependent changes in performance that are poorly understood and often attributed vaguely to “surface reconstruction” or “oxidation/reduction”[2]. This limited view treats catalyst surfaces as quasi-static actors and overlooks that the catalyst’s structure， composition， and electronic state commonly evolve actively under reaction conditions.
The electrochemical reduction of nitrate （NO3-） to ammonia （NH3） has gained significant attention in recent years due to its dual environmental and industrial importance[3-6]. Nitrate pollution， particularly from agricultural runoff and industrial effluents， has emerged as a critical global challenge， contaminating water resources and posing a threat to ecosystems and human health[7-9]. In this regard， the efficient conversion of NO3- into NH3 presents a promising solution， as NH3 can be used not only as a fertilizer but also as a potential carrier for hydrogen energy[10]. However， the electrocatalytic nitrate reduction reaction （NO3RR） is accompanied by thermodynamic and kinetic challenges， primarily due to the competition with the hydrogen evolution reaction （HER） and the eight-electron reduction pathway required for NO3RR[11-13]. Addressing these challenges is essential to optimizing the process and developing sustainable， green ammonia synthesis technologies.
Among various materials explored for NO3RR， copper-based catalysts have gained substantial attention due to their distinct catalytic properties[14-17]. Copper is one of the few metals that can facilitate the eight-electron reduction of NO3- to NH3， offering a direct path toward the desired product[18]. The multi-valency of copper is a key feature that sets it apart from other potential catalysts. In its metallic state （Cu0）， copper exhibits a high level of catalytic activity， while its ionic states （Cu+ and Cu2+） contribute to different aspects of the electrochemical process[19-21]. These oxidation states enable copper to participate in the necessary electron transfer steps for NO3RR， making it an attractive candidate. Another important property of copper is its ability to effectively adsorb NO3-， a critical factor for promoting the reaction[22-23]. Optimal adsorption energy ensures that NO3- is held long enough on the surface to undergo the necessary reduction steps， without binding too strongly， which would hinder desorption and reactivity. However， while these advantages make copper a promising candidate， they come with a notable challenge. Copper catalysts are often prone to structural instability during electrochemical reactions， especially under the harsh operating conditions[24-28]. This instability can lead to surface oxidation， phase segregation， or the dissolution of copper from the electrode， which compromises catalytic activity and accelerates catalyst deactivation. The delicate balance between achieving high activity and maintaining structural stability remains one of the most significant hurdles in copper-based catalysis.
The term “dynamic evolution” refers to the continuous and often complex changes in the structure and composition of copper-based catalysts during electrochemical reactions. These changes can include phase transformations， fluctuations in oxidation states， and the migration of atoms or ions within the catalyst， all of which are closely tied to the catalytic activity and stability[29-31]. A key phenomenon within this dynamic evolution is the oscillation of copper’s valence states[32-33]， which allows for tuning the catalyst’s reactivity. A well-controlled dynamic evolution， in which these valence state changes are carefully managed， can lead to enhanced catalyst performance by optimizing the adsorption of $\mathrm{N}{\mathrm{O}}_{3}^{-}$， improving electron transfer efficiency， and stabilizing intermediate species. However， when this evolution is uncontrolled， it can lead to catalyst degradation. Unstable oscillations in oxidation states， excessive oxidation， or phase transitions can result in the loss of active sites， decreased catalytic efficiency， and even complete catalyst failure[34-35]. This degradation is often observed as a decline in ammonia yield and selectivity， as well as an increase in side products， such as nitrogen gas or hydrogen. The ability to control the dynamic evolution of copper catalysts， therefore， becomes critical in maximizing their efficiency and durability for NO3RR applications.
This review highlights a novel perspective： that controlled dynamic evolution is not a drawback but a crucial strategy for enhancing the performance of Cu-based catalysts in NO3RR. This work provides a comprehensive review of the dynamic evolution mechanisms of Cu-based catalysts during NO3RR. We emphasize how advanced techniques like operando spectroscopy and microscopy are used to track these changes in real time. More importantly， we systematically summarize and critically discuss the latest strategies for regulating this dynamic evolution， including： valence-state control， morphological engineering， crystal facet optimization， heterogeneous interface construction and in-situ defect engineering. These strategies effectively harness the dynamic changes to boost catalytic activity， selectivity， and durability. Reframing dynamic evolution from a challenge to a powerful design principle for designing highly efficient and durable catalysts for sustainable ammonia production.

2 Mechanism of evolution

The dynamic evolution of catalysts is a critical process in electrocatalysis， particularly for the electrochemical NO3​RR （Fig.1）. This phenomenon involves continuous changes in the catalyst’s structure and composition under reaction conditions. These changes can either enhance or hinder performance， making their controlled management crucial for achieving a desired active surface. The active phase of the catalyst is often a transient or reconstructed state， and its dynamic transformation is a key design strategy for selective and durable ammonia electrosynthesis[36-37]. This evolution is not merely a side effect of the reaction； it is a self-optimization mechanism where the catalyst surface spontaneously reorganizes to minimize its interfacial free energy. This process leads to the formation of a metastable but highly active state with tailored electronic properties， optimized adsorption geometries， and defect-rich configurations that the pristine catalyst lacks.

2.1 Factors driving evolution

The dynamic evolution of catalysts is a complex phenomenon governed by a sophisticated interplay of both intrinsic catalyst properties and extrinsic environmental factors. This continuous， in-situ refinement of the catalyst structure is not a passive process but is actively driven by the conditions within the electrochemical cell， ultimately leading to the progressive enhancement of catalytic efficiency.
A primary driver of this evolution is the applied potential during the reaction. The potential directly modulates the electron density at the catalyst-electrolyte interface， which in turn influences the oxidation state of the metal atoms， the surface morphology， and the adsorption energies of various reaction species[38-39]. This constant electrochemical stress can induce surface reconstruction and the formation of new， often more active， phases that are thermodynamically favorable only under reaction conditions. This is often seen in copper catalysts， where the interplay between Cu0 and Cu+ is crucial for the reaction pathway[40-41]. Complementing the electrical influence， the external environmental conditions such as the electrolyte’s composition and pH are equally critical. The presence of specific anions or cations can dictate the local chemical environment， promoting or inhibiting surface restructuring and influencing the stability of the active sites. For instance， changes in pH can alter the proton-coupled electron transfer steps， directly affecting the formation of ammonia and the overall efficiency. Furthermore， the intrinsic composition of the catalyst plays a central role. In complex systems like bimetallic or alloy catalysts， the applied potential and surrounding environment can induce a redistribution of the metal components， leading to surface segregation or dealloying. This dynamic compositional change can expose new active sites or create interfaces with synergistic catalytic properties. Finally， the reaction intermediates themselves act as powerful agents of change. The adsorption and desorption of species like nitrate （NO3-）， nitrite （NO2-）， or even the final product， ammonia， can cause surface strain and local reconstruction， effectively “coaxing” the catalyst into a more optimal configuration[42-44]. These intermediates donot just react on the surface； they fundamentally reshape it， creating a self-reinforcing loop where the catalyst adapts to the very process it is facilitating. All these factors， potential， environment， composition， and intermediates， do not operate in isolation but rather form an intricate feedback loop that drives the catalyst toward a progressively refined and highly active state.

2.2 Type of evolution

Dynamic evolution in copper-based catalysts during the NO3RR typically involves a complex and interlinked series of structural and chemical transformations. These changes are not random but are influenced by a combination of internal and external factors， including the reaction environment， the catalyst’s intrinsic properties， and the duration of the reaction. These transformations can be broadly categorized into structural and chemical dynamics （Table 1）.

2.2.1 Structural transformations

Structural transformations refer to the physical changes in the atomic or crystalline structure of the catalyst that occur as a direct result of external forces such as applied potential， temperature， and interactions with reaction intermediates.
A common form of this is surface reconstruction， where the catalyst’s outermost atomic layers reorganize to a new， often more stable or catalytically active， configuration[45-46]. This process can expose new crystal facets or create more efficient adsorption geometries for key reactants. For example， a planar surface might be reconstructed to expose more steps and kinks， which are known to be highly active sites. Additionally， catalysts can undergo phase transitions， where the bulk material transforms from one crystalline phase to another under specific electrochemical conditions. This is particularly relevant for copper， where shifts between the metallic Cu0 phase and the oxide phases like Cu2​O or CuO can drastically alter the electronic properties and reactivity of the surface[47-48]. Beyond atomic-scale changes， morphological evolution at the nanoscale is also a key feature of dynamic behavior [49]. Nanoparticles may change their size or shape， for instance， from spheres to more cubical or rod-like structures. Such morphological changes can significantly impact the surface area， the distribution of active sites， and the overall efficiency of mass transport. Finally， nanoparticle-based catalysts are susceptible to aggregation/de-agglomeration， a process where individual particles either cluster together or break apart[50-51]. While aggregation can reduce active surface area and hinder electron transport， a controlled level of de-agglomeration can expose new active sites， making this a critical aspect of managing long-term catalyst stability.

2.2.2 Chemical state dynamics

Intertwined with structural changes are the chemical state dynamics， which involve the variation in the oxidation state， electronic structure， and coordination environment of the metal centers. These chemical changes are critical for modulating the catalyst’s reactivity and selectivity.
Oxidation state changes are a hallmark of copper catalysts in NO3​RR， where the copper can fluctuate between its metallic form （Cu0） and its oxidized states （Cu+ and Cu2+） depending on the applied potential and the local chemical environment[52-53]. The ability to dynamically interconvert between these states is crucial for facilitating different steps of the reaction pathway. This is because specific intermediates may bind preferentially to one oxidation state over another. Simultaneously， these oxidation state changes are accompanied by electronic structure adjustments， as the electron density distribution at the catalyst surface shifts. These adjustments affect how the catalyst interacts with reactants and intermediates， influencing binding energies and reaction kinetics[54-56]. The electronic structure of the copper surface can be fine-tuned to promote the stronger adsorption of nitrate， which improves the overall reaction rate. A final element of chemical dynamics is ligand or surface atom exchange， particularly in multi-component catalysts[57-59]. The atoms at the surface of an alloy or bimetallic catalyst may rearrange or exchange with species from the electrolyte， altering the surface composition and creating new active sites. This process is a key part of the catalyst’s ability to self-optimize and adapt to its electrochemical environment.

3 Characterizations of dynamic evolution

Understanding the dynamic evolution of copper-based catalysts during the NO3​RR requires advanced characterization techniques that can capture changes in real-time and under operando conditions. This section details the primary electrochemical characterization techniques， potential mechanisms， and instrumental methods used to probe these dynamic transformations， accompanied by illustrative examples.

3.1 Electrochemical characterizations

Electrochemical techniques are fundamental for assessing catalyst performance and understanding NO3RR mechanisms. They provide insights into reaction kinetics， surface processes， and the efficiency of the catalytic system. This operando insight elucidates how active sites emerge， deactivate， and regenerate， providing design principles for constructing highly selective and durable copper-based NO3RR catalysts.
Cyclic voltammetry （CV） is a powerful technique for studying redox processes， surface adsorption/desorption， and reaction mechanisms. By sweeping the potential over a range and measuring the resulting current， CV can identify distinct redox peaks corresponding to changes in the oxidation state of copper （Cu0↔ Cu+ ↔ Cu2+） and the adsorption/desorption of reaction intermediates. Paliwal et al.[60] conducted CV， rotating-disk voltammetry and Pb-UPD stripping to track the dynamic oxidation state of Cu during NO3- RR in 0.5 mol/L KOH. The Cu-only electrode displayed anodic peaks at 0.74~1.17 V vs RHE （Cu → Cu2O → Cu（OH）2/CuO） followed by a broad cathodic feature from +0.30 to -0.35 V （Cu（Ⅱ/Ⅰ） → Cu0）， indicating extensive surface oxidation during reaction （Fig. 2a）. In contrast， the CuAg alloy shifted the Cu（Ⅰ） → Cu0 reduction onset negatively by ~75 mV， and Pb-UPD revealed a suppressed oxide stripping charge， evidencing that Ag stabilized Cu（Ⅰ） against over-oxidation. Steady-state polarization showed the CuAg lowered the NO3RR onset by 120 mV and doubled the kinetic current density compared to neat Cu， confirming that electrochemically modulated Cu oxidation state directly governed activity.
Monitoring CV curves over repeated cycles or extended periods can reveal the stability of the catalyst and identify whether the active species are transient or stable. For example， a shift in reduction potential or a decrease in current density for nitrate reduction might indicate catalyst deactivation or a change in the dominant reaction pathway. Zhang et al.[61] employed CV to trace the potential-driven reconstruction of a single Co-B/Ru12 particle mounted on a carbon nanoelectrode （Fig. 2b~d）. Ten successive CV scans （50 mV/s， 0.1 mol/L NaOH + 0.1 mol/L NaNO3） revealed a continuous increase in the nitrate-reduction plateau current starting from the second cycle， indicating that the number of active sites rose monotonically with each sweep. Identical-location TEM performed after every five CV cycles showed progressive Co leaching and structural loosening directly correlated with the electrochemically observed activation， thereby demonstrating that CV serves as a sensitive in-situ probe for catalyst restructuring during the NO3RR. Moreover， linear sweep voltammetry （LSV） also can be used to monitor the dynamic changes in electrochemical properties under varying voltage conditions[62]. In this regard， LSV was employed to track the in-situ electrochemical conversion of CuBr into Cu-Br during NO3RR （Fig.2e）. A pronounced cathodic peak at 0.4 V vs. RHE appeared in the first scan and progressively decayed until it vanished after the fourth sweep， signaling rapid reduction of CuBr to metallic Cu accompanied by Br leaching. Subsequent LSV runs recorded in 0.1 mol/L PBS containing 50 mg/L NO3- revealed markedly higher current densities for the halogen-modified surfaces compared to Cu—O， corroborating the formation of more active Cu—Br sites. The evolution of LSV profiles thus provided direct electrochemical evidence for the dynamic surface reconstruction and activation of the CuBr precursor under operating conditions.
Constant current （CP） and constant potential （CA） methods are powerful and widely used in-situ electrochemical characterization tools for monitoring the dynamic evolution of Cu-based electrocatalysts under operational conditions. By recording the variations in potential or current over time， these techniques provide real-time， sensitive， and quantitative insights into the evolution of catalyst activity and stability， such as deactivation， activation， and reconstruction processes. Wang et al[63]. employed CA measurements at -0.85 V vs. SCE to monitor the dynamic evolution of CuO nanowire arrays during NO3RR. A sharp current surge within the first minutes signaled the rapid electrochemical reduction of CuO to Cu/Cu2O， while the subsequent stabilization and overlap of i-t traces in cycles 2~4 evidenced the irreversible formation of a steady， catalytically active phase （Fig. 2f）. This real-time current profile directly correlated with the structural transition and provided an unambiguous electrochemical fingerprint of the Cu oxidation-state evolution under working conditions. By analyzing the current decay in CA experiments， insights can be gained into the dynamic changes in active sites， such as the formation of an inactive oxide layer or the reconstruction of the surface to a more active state.
Electrochemical methods are powerful and widely used in-situ characterization tools for monitoring the dynamic evolution of electrocatalysts under working conditions. By recording changes in potential or current， they provide real-time， sensitive， and quantitative insights into the evolution of catalyst activity and stability. However， these methods offer indirect information regarding macroscopic performance changes. To gain a deeper understanding of the underlying microscopic mechanisms， it is essential to combine electrochemical results with in-situ/operating condition spectroscopy， mass spectrometry， scattering techniques， and physicochemical characterization. This multi-technique approach is a core strategy for studying the dynamic behavior of catalysts.

3.2 Microstructure characterizations

Microstructure characterization focuses on the structural evolution of materials at different length scales， from atomic arrangements to macroscopic morphology[64]. The dynamic changes in the microstructure， such as grain growth， phase transformation， and interface evolution， often correlate with performance characteristics and degradation mechanisms[65-67].
Scanning electron microscopy （SEM） offers high-resolution imaging to monitor surface morphology， microcracks， porosity， and phase boundaries. Meanwhile， transmission electron microscopy （TEM） enables atomic-level resolution for studying the fine details of microstructure evolution， such as the formation of new phases， defects， or interfaces. Zhang et al.[68] used SEM and TEM to track the electrochemical reconstruction of Cu-based catalysts during NO3RR （Fig.3a， b）. Ex-situ SEM images of commercial CuO nanoparticles revealed a faceted， irregular morphology that remained largely intact after 2 h of electrolysis at -0.68 V vs RHE， confirming the oxide-derived nature of the active phase. TEM further showed that the initially polycrystalline CuO domains were progressively reduced to metallic Cu， as evidenced by the appearance of Cu（111） lattice fringes （d = 0.21 nm） and the disappearance of CuO reflections. Post-reaction TEM also disclosed the emergence of grain boundaries and surface roughening， consistent with the formation of distinct Cu（111） and Cu（100） facets proposed to host Site A （NO3- → NO2-） and Site B （NO2- → NH3）， respectively. Another work demonstrated that the CuO/Co3O4@NC precursor underwent a cathodic-potential-driven structural reconfiguration during nitrate electroreduction using TEM （Fig. 3c， d）[69]. High-resolution TEM images captured after the reaction showed clear lattice fringes corresponding to Cu2O（110） （0.301 nm） and Cu2O（200） （0.214 nm）， confirming the partial reduction of CuO to Cu2O， whereas the Co3O4（311） spacing （0.243 nm） remained unchanged， indicating its structural stability. Selected-area electron diffraction （SAED） rings were indexed to the （111） and （311） planes of Co3O4 and the （110） and （200） planes of Cu2O， corroborating the in-situ formation of the Cu2O/Co3O4 heterointerface. Post-cycling TEM further demonstrated that the nanoparticle morphology and crystalline lattice fringes were preserved after 12 consecutive runs， evidencing the high structural robustness of the reconstructed catalyst.
Electron-transparent electrochemical cells integrated with liquid-phase TEM uniquely enabled simultaneous application of potential and direct visualization of the same catalysts at near-operando conditions[70]. This configuration bridged the gap between ensemble-averaging spectroscopies and single-particle imaging， allowing quantitative correlation between applied potential， local morphology， and chemical state. Using such electrochemical liquid-cell TEM， operando transmission X-ray microscopy and correlated X-ray absorption spectroscopy， Yoon et al. tracked the real-time morphological and compositional evolution of Cu2O cubes during NO3RR （Fig.3e， f）. They observed that the cubes remained intact at -0.2 V vs. RHE， whereas from -0.3 to -0.5 V the primary restructuring mechanism was sequential dissolution followed by redeposition of metallic copper nanoparticles. At -0.6 V， direct reduction to metallic Cu became dominant. EC-TEM time-series revealed that corners and edges dissolved first， leading to hollow， roughened structures or complete disappearance of the original cubes.

3.3 Spectroscopic characterizations

The changes in components during the reconstruction of electrocatalysts lead to the formation of new chemical bonds， which can be analyzed using advanced spectroscopy techniques such as X-ray diffraction （XRD）， X-ray photoelectron spectroscopy （XPS）， synchrotron X-ray absorption spectroscopy， Raman spectroscopy， and mass spectrometry （MS）. Spectroscopic techniques offer exceptional sensitivity to subtle material property changes， making them essential for studying the fine details of dynamic evolution， including bond breaking， electronic restructuring， and surface interactions.
Manna et al[71]. used XRD to track the electrochemically driven phase transition of CuO/CNx during NO3RR （Fig.4a）. Before electrolysis， the pattern displayed sharp reflections at 2θ = 32.5°， 35.6°， 38.9°， 46.5°， 48.8°， 53.7°， and 58.4°， all indexed to the （110）， （002）， （111）， （-112）， （-202）， （020）， and （-113） planes of monoclinic CuO （JCPDS 48-1548）. After 5 min of chronoamperometry at -0.5 V vs RHE， the CuO peaks vanished and new， intense peaks emerged at 43.2°， 50.3°， and 74.1°， corresponding to the （111）， （200）， and （220） planes of metallic Cu （JCPDS 04-0836）. The complete disappearance of CuO signatures and the exclusive appearance of Cu reflections confirmed the in-situ electrochemical reconstruction of CuO into under-coordinated metallic Cu nanostructures that served as the active phase for selective nitrate-to-ammonia conversion. XPS was employed to probe the in-situ evolution of surface chemistry during NO3RR （Fig.4b）. Depth-profiling XPS revealed that the outermost layers of the FeNi alloy nanoparticles were initially passivated by mixed Fe（Ⅱ）/Fe（Ⅲ） and Ni（Ⅱ） oxides， while the core remained metallic[72]. After 24 h of continuous electrolysis at -1.3 V vs SCE， the intensities of the Fe 2p peaks at 706.8 eV （Fe0） and 852.6 eV （Ni0） increased markedly from the surface inward， evidencing progressive reduction of the oxide shell. These XPS results directly corroborated the dynamic reconstruction of the oxide-passivated FeNi surface into a more metallic and vacancy-rich active phase responsible for sustained nitrate-to-ammonia conversion. Fourier-transform extended X-ray absorption fine structure （FT-EXAFS） spectroscopy was employed to monitor the dynamic structural evolution of Cu@CuTCNQ during electrochemical nitrate reduction （Fig.4c）[73]. In the pristine CuTCNQ precursor， the R-space spectrum exhibited a single prominent shell at 1.94 Å corresponding to Cu—N coordination， and the absence of any Cu—Cu scattering peak confirmed the monodisperse Cu（Ⅰ） centers. After 2 h of potentiostatic electrolysis at -0.6 V vs. RHE， a new Cu—Cu shell emerged at 2.54 Å， while the Cu—N shell simultaneously diminished in intensity. This transition indicated a progressive electrochemical reconstruction in which Cu（Ⅰ） ions were reduced and aggregated into metallic Cu nanoparticles hosting abundant vacancy defects， thereby validating in-situ formation of the active Cu phase responsible for the observed 96.4% NH3 Faradaic efficiency.
Daiyan et al.[74] employed operando Raman spectroscopy to track the potential-dependent phase evolution of defect-engineered CuO during NO3RR （Fig.4d）. Upon stepping the potential cathodically from open-circuit to -0.6 V vs RHE， the characteristic CuO band at ~300 cm-1 progressively vanished while a new Cu2O band at ~420 cm-1 emerged， evidencing an in-situ partial reduction of CuO to Cu2O. This conversion was fully reversed when the cathodic bias was removed， confirming the dynamic and reversible nature of the surface redox chemistry. Thus， the active surface was identified as a mixed-valent CuOx ensemble whose composition was tuned in real time by the applied potential， providing direct spectroscopic evidence for the dynamic evolution of copper oxidation states during nitrate electroreduction. Zhou et al.[75] employed in-situ Raman and operando ATR-FTIR spectroscopy to track the potential-dependent structural evolution of Cu2O cubes during NO3RR （Fig.4e， f）. Time-resolved Raman spectra recorded at -0.6 V vs RHE preserved the Cu2O phonon modes at 221， 419 and 524 cm-1， whereas within 15 min at -0.9 V these bands vanished and NH3-associated vibrations at ~936 cm-1 emerged， evidencing complete reduction to oxide-derived Cu0. Complementary ATR-FTIR detected an increasing negative feature at 1350 cm-1 （adsorbed NO3-） and the sequential appearance of 1250 cm-1 （NO2-） and 1450 cm-1 （*NH2） intermediates， confirming the stepwise hydrodeoxygenation pathway. Post-reaction XRD further corroborated the irreversible transformation from Cu/Cu2O to metallic Cu0 under high cathodic bias， validating the spectroscopically observed active phase.

4 Regulation strategies for copper dynamics

4.1 Valence-state control

The valence state of copper （e.g.， Cu0， Cu+， Cu2+） is a pivotal factor governing the catalytic activity and selectivity of copper-based catalysts in NO3RR[71,76]. Valence states directly influence the electronic structure of active sites， modulating the adsorption energy of key intermediates （e.g.， NO3-， NO2-， *NH2OH） and the activation barriers of critical reaction steps. Dynamic shifts in valence states under applied potentials or reaction conditions further highlight the need for deliberate control strategies to stabilize or manipulate these states for optimal performance[75,77-78].
Dynamic redox cycling between Cu0 and Cu2+ was constructed in-situ to eliminate the high overpotential of NO3RR. Han et al.[79] demonstrated that Ru15Co85 hollow nanododecahedrons underwent a spontaneous redox step （Co0 + NO3- → Co2+（OH）2 + NO2-， ΔG = -303 kJ/mol） that bypassed the sluggish NO3- → NO2- electron-transfer barrier （Fig.5a~c）. Under cathodic bias， the resulting Co（OH）2 was rapidly reduced back to Co0， while *NO2 was hydrogenated to NH3 on adjacent Ru sites that supplied active *H. This Cu-like valence shuttle lowered the onset potential to +0.4 V vs RHE， achieved 97% NH3 Faradaic efficiency and operated stably at 1 A/cm2， illustrating how in-situ regulation of metal oxidation states decoupled the kinetically demanding N—O cleavage from subsequent proton-coupled steps in nitrate-to-ammonia conversion.
Electrochemical redox activation of Cu-Co binary sulfides produced a tandem Cu/CuOx-Co/CoO architecture in which the in-situ valence dynamics of Cu and Co dictated the reaction sequence （Fig.5d~f）[80]. At > -0.2 V vs RHE， Cu-based phases （Cu0/Cu2+） preferentially reduced NO3- to NO2-， while adjacent Co-based phases （Co2+/Co3+） rapidly hydrogenated NO2- to NH3， circumventing scaling relations. Operando Raman and SECM confirmed that NO2- generated on the Cu layer diffused to the Co layer for further reduction. This valence-mediated tandem mechanism delivered 93% NH3 Faradaic efficiency and 1.17 mmol/（cm2·h） at -0.175 V， demonstrating that controlled redox cycling of Cu and Co oxidation states effectively promoted low-overpotential nitrate-to-ammonia conversion.
Bai et al.[81] demonstrated that Cu2O nanocubes underwent potential-dependent dynamic reconstruction during NO3RR （Fig.5g， h）. At low overpotentials （~0.1 V vs RHE）， quasi-in-situ XPS and operando XAS revealed a predominance of Cu（I） （>70%） both on the surface and in the bulk， which selectively catalyzed the two-electron reduction of NO3- to NO2- with a FE of ~75%. When the potential was shifted to more negative values （~-0.3 V vs RHE）， the Cu（Ⅰ） species were almost completely reduced （>90%） to metallic Cu（0）， accompanied by pronounced particle aggregation and crystallinity loss. This metallic Cu（0） phase strongly correlated with the high FE （~88%） for multi-electron conversion of NO3-/NO2- to NH3. The authors further used in-situ Raman spectroscopy to identify hydroxylamine （NH2OH） as a key intermediate on metallic Cu（0）， confirming that the dynamic transition from Cu（Ⅰ） to Cu（0） is essential for efficient NH3 production.

4.2 Morphological engineering

Dynamic morphological transitions， including particle reshaping， sintering， or the agglomeration of single atoms into clusters/particles， further highlight the need for strategic control to stabilize favorable structures and suppress deleterious transformations[82-84]. These morphological features directly impact accessibility to active sites， electron transfer efficiency， and the selectivity of nitrate reduction pathways， making morphological engineering a versatile lever for optimizing catalytic performance.
In recent years， numerous single-site Cu catalysts were engineered for NO3RR. However， under the strongly reductive potentials required for nitrate reduction， these dispersed metal atoms often underwent dynamic structural changes， ranging from the reconstruction of their coordination sphere to the in-situ aggregation into metallic nanoparticles， thereby obscuring the identity of the true active sites and complicating the mechanistic interpretation. Yang et al. revealed that the original Cu-N4 single-atom catalyst progressively restructured into ~5 nm Cu0 nanoparticles when the potential was swept from 0.00 to -1.00 V vs RHE during NO3RR （Fig.6a~c）[50]. Operando XAS tracked a concurrent rise in Cu0 content and ammonia Faradaic efficiency， peaking at 84.7% （4.5 mg/（cm2·h）） at -1.00 V. HAADF-STEM confirmed that these potential-driven Cu0 nanoparticles， rather than the initial single-atom sites， served as the true active centers； after electrolysis， the particles disintegrated and reverted to Cu-N4 upon air exposure， demonstrating a fully reversible morphology cycle. Likewise， Shen et al.[85] reported that single Cu atoms anchored on hierarchical N-doped carbon nanocages （Cu1/hNCNC） underwent potential-driven assembly into Cu3-N4 clusters during nitrate electroreduction （Fig. 5d~f）. Operando XAS showed a progressive increase in Cu-Cu coordination and the emergence of a Cu3-N4 moiety at -0.58 V vs RHE， coincident with an ultrahigh NH3 yield of 99.4 mol/（h·g） （Cu） and 99.3% Faradaic efficiency. These in-situ-formed clusters exhibited stronger NO3- adsorption and lower protonation barriers than the initial Cu-N4 sites， while the hierarchical nanocage architecture ensured rapid mass and charge transfer. After electrolysis， exposure to air redispersed the clusters back to isolated Cu-N4， demonstrating a fully reversible morphology cycle that underpinned the exceptional activity and stability.
Yu et al.[86] demonstrated that an electro-chemical post-modification based on multi-potential-step treatment could dynamically restructure pre-formed Cu nanodendrites （Fig.6g~i）. During 3600 s of alternating oxidation （0.85 V vs Hg/HgO） and reduction （-0.5 V） pulses， surface Cu atoms were repeatedly dissolved and re-deposited， yielding a markedly rougher surface without altering the overall dendritic morphology. This in-situ rearrangement enlarged the electrochemically active surface area from 0.137 to 0.529 mF/cm2 and created a high density of low-coordination sites. Consequently， the MPS-treated Cu nanodendrites delivered an NH3 yield of 0.224 mmol/（h·cm2） at -1.2 V vs SCE with the FE of 94.4%， outperforming the pristine counterparts and maintaining structural integrity over seven successive cycles.

4.3 Crystal facet optimization

Crystal facets of copper catalysts exhibit distinct surface energies， electronic configurations， and adsorption properties， which profoundly influence their reactivity toward nitrate reduction [87-89]. Different facets interact uniquely with reaction intermediates： for example， low-index facets may favor nitrate activation， while stepped or kinked facets could enhance ammonia desorption. Controlling the exposure of specific crystal facets allows for targeted optimization of rate-determining steps in NO3RR， making facet engineering a precision tool for selectivity control.
A key strategy for enhancing electrochemical nitrate-to-ammonia conversion lies in the in-situ dynamic regulation of copper-based catalysts to expose specific facets， as demonstrated by the reconstruction of ultrathin CuO nanobelts under NO3RR[90]. Electrochemical reduction of CuO nanobelts that initially presented dominant （111） facets in a NO3-- containing alkaline electrolyte at -0.4 V vs RHE yielded Cu nanobelts （Cu-NBs-100） that preferentially exposed Cu（100） facets alongside abundant surface defects （Fig. 7a~c）. This facet evolution was mediated by NO3RR intermediates （e.g.， N*）， which acted as in-situ capping agents because of their stronger adsorption on Cu（100） relative to Cu（111） and thereby drove the reconstruction of CuO（111） to Cu（100）. The synergy between Cu（100） facets and defects upshifted the Cu d-band center， intensified the adsorption of NO3- and key intermediates， and strengthened H* adsorption to suppress the competitive HER. Consequently， Cu-NBs-100 delivered a maximum NH3 production rate of 650 mmol/（g·h） in alkaline media， 2.3-fold higher than the Haber-Bosch process， while maintaining a FE exceeding 95% at 0.15 V vs RHE and exhibiting robust stability over 20 h of operation. This in-situ facet engineering underscored the critical role of reaction-intermediate-mediated reconstruction in tailoring catalyst surface structures for efficient NO3RR.
An alternative approach to NO3RR performance involves the in-situ dynamic modulation of copper-based catalysts to selectively expose specific facets for efficient facet tandem catalysis （Fig.7d~f）[91]. Operando characterizations and density functional theory （DFT） calculations converged on the understanding that the superior performance of in-situ-reduced Cu nanosheets was attributed to the deliberate exposure of a Cu（100）/Cu（111） facet tandem. During NO3RR， CuO nanosheets were cathodically reduced to metallic Cu， and the surface was reconstructed into a balanced combination of Cu（100） and Cu（111） domains （Cu（111）/Cu（100） = 1.26）. The Cu（100） facet lowered the adsorption energy of *NO3 （ΔG = -0.22 eV compared to -0.02 eV on Cu（111））， thereby facilitating the initial deoxygenation step to *NO2. In contrast， the Cu（111） facet stabilized *NO2 （ΔG = -1.76 eV） and lowered the energy barrier for its hydrogenation to *NOH （ΔGPDS = 0.32 eV vs 0.35 eV on Cu（100））. This cooperative facet sequence enhanced the NH3 partial current density to 665 mA/cm2 （1.41 mmol/（h·cm2）） at -0.59 V vs RHE， while effectively suppressing the HER， demonstrating how electrochemically guided facet engineering dynamically tuned the catalytic performance for NO3RR.

4.4 Heterogeneous interface construction

Heterogeneous interfaces assemble two distinct components into asymmetric architectures， where strong electronic coupling at the boundary optimizes the surface electronic structure and enhances catalytic activity. Interfaces can modulate electron transfer between components， stabilize favorable copper valence states， or create synergistic adsorption sites for intermediates， thereby altering reaction pathways and suppressing deactivation [92-96].
In-situ electrochemical restructuring of CuO transformed the pristine spherical aggregates into a layered Cu/Cu（OH）2 heterointerface that orchestrated synergistic NO3RR activity （Fig.8a~c） [97]. During cathodic polarization， oxygen-rich vacancies on the initial CuO accelerated *NO2 generation and local alkalization， driving the phase transition to metallic Cu（111） nanodomains interleaved with Cu（OH）2（021） nanosheets. DFT calculations revealed that Cu（OH）2（021） lowered the nitrate-adsorption barrier to -0.62 eV （vs +0.55 eV on Cu（111）） and shifted the potential-determining step to *NO2 protonation （0.24 eV）， while Cu（111） facilitated subsequent hydrogenation steps. Meanwhile， the hydroxide phase suppressed the competing HER by destabilizing *H. This dynamic heterointerface delivered an NH3 yield of 15.53 mg/（h·mg）（cat） at -0.80 V vs RHE with 90.7% Faradaic efficiency， illustrating how the in-situ formation of a Cu/Cu（OH）2 dual-phase architecture was exploited as a powerful regulator for selective nitrate-to-ammonia conversion.
Xia et al.[98] reported that a Cu/Fe bimetallic catalyst （Cu5Fe@NC） underwent in-situ self-evolution during NO3RR to form a CuxO/Fe3O4 heterojunction interface （Fig.8d~f）. Upon initial activation， metallic Cu and Fe phases were progressively oxidized to Cu2O， CuO， and Fe3O4， creating a spatially continuous Cu-O-Fe linkage that facilitated electron redistribution and accelerated interfacial electron transfer. In-situ Raman and FTIR spectroscopy revealed that NO3- was first adsorbed in a bridging mode across Cu and Fe sites， then sequentially reduced to NO2-， *NO， NH2OH， and finally NH3. DFT calculations confirmed the CuxO/Fe3O4 interface lowered the Gibbs free-energy barrier for *NO → *NHO conversion from 0.19 eV on Cu2O alone to -1.01 eV， thereby suppressing intermediate accumulation and boosting NH3 selectivity to 92.61% at -0.7 V vs RHE.
Another study reported that a Cu2O/Cu nanosheet interface was created in-situ during NO3RR without any pre-reduction or post-calcination steps （Fig.8g~i）[99]. Constant-current anodic oxidation of Cu foam first yielded a CuO/Cu precursor， which underwent self-reduction at -0.45 V vs RHE to generate the active Cu2O/Cu heterojunction. In-situ Raman spectroscopy tracked the progressive disappearance of CuO/Cu（OH）2 and the emergence of Cu2O， while HRTEM， XPS and AES confirmed the coexistence of Cu（0） and Cu（Ⅰ） after reaction. The reconstructed interface exhibited a 26.3 mF/cm2 double-layer capacitance and 658 cm2 ECSA， providing abundant active sites and the lowest charge-transfer resistance among all studied states. DEMS and DFT revealed that the Cu2O/Cu heterojunction accumulated interfacial electrons， weakened N―O bonds through π* antibonding orbital filling， and lowered the free-energy barrier for *NO2→*NO from +0.63 eV on bare Cu to -0.16 eV. Simultaneously， the interface tuned *H adsorption （-0.47 eV vs -0.26 eV on Cu） to suppress HER， enabling 93.2% NH3 Faradaic efficiency and 2793 μg/（h·cm2） yield over 25 stable cycles.

4.5 Defect Engineering

Defects in copper-based catalysts， particularly those formed in-situ under dynamic electrochemical conditions during NO3RR， play a key role in regulating catalytic activity， selectivity， and durability. Unlike pre-synthetically introduced defects， which may evolve or diminish under reaction stress， in-situ dynamically formed defects， such as surface vacancies， transient dislocations， and electrochemically induced grain boundaries， emerge as responsive active sites[100-101]. These defects form in response to applied potentials， intermediate adsorption， or structural rearrangements， dynamically adjusting the local electronic environment and coordination geometry of copper atoms. By enhancing electron density at adjacent catalytic sites， stabilizing high-energy intermediates （e.g.， *NO2， *NH2）， and lowering activation barriers for key reaction steps， in-situ defects act as “dynamic regulators” that adapt to the evolving reaction process.
A critical aspect of enhancing electrochemical nitrate-to-ammonia conversion lies in the in-situ dynamic regulation of copper-based catalysts to generate stacking faults during the NO3RR. As demonstrated in oxide-derived copper systems， pristine Cu oxides （CuO or Cu2O） underwent electrochemical reduction under cathodic potentials and transformed into metallic Cu while in-situ generating abundant stacking faults （Fig.9a~d）[102]. This structural evolution was driven by the release of oxygen ions from the oxides during reduction， leading to non-stoichiometry that induced stacking faults to accommodate lattice perturbations. Physical characterizations confirmed that these stacking faults were well-retained after reaction， with tensile strains distributed along the fault regions as revealed by geometrical phase analysis. The presence of stacking faults significantly boosted catalytic performance. This oxide-derived Cu electrode achieved 93% nitrate-N removal， 94% ammonia-N selectivity， and ammonia Faradaic efficiencies up to 80%， outperforming pristine Cu foam. DFT calculations clarified that tensile strains from stacking faults strengthened nitrate adsorption and suppressed the competitive HER by enhancing H* adsorption， thereby accelerating NO3RR kinetics. Moreover， these defects increased the number of low-coordinated surface sites， optimizing interactions with reaction intermediates. Notably， the stacking faults remained stable over multiple cycles， ensuring consistently high activity. This in-situ defect engineering strategy highlighted the pivotal role of dynamically generated stacking faults in tailoring Cu-based catalysts for efficient NO3RR.
Another representative example was the fabrication of an amorphous/crystalline dual-phase Cu （a/c-Cu） catalyst through in-situ electrochemical reduction of air-annealed Cu foam （Fig.9e~g）， which induced the formation of abundant low-coordinated Cu sites within stable amorphous domains [103]. Structural characterizations via operando XANES， HRTEM， and Pb-UPD confirmed that these in-situ-generated defects were retained during NO3RR， preventing crystallization of the metastable amorphous phases. Mechanistic studies revealed that the amorphous domains with low-coordinated sites played two critical roles： they optimized the adsorption of nitrogen-containing intermediates （e.g.， *NO3， *NO2） and facilitated the generation of active hydrogen species from water dissociation， thereby accelerating reaction kinetics. Control experiments with H2-annealed a/c-Cu-H2 catalysts， which exhibited reduced amorphous domains and fewer low-coordinated sites， demonstrated a significantly decreased NH3 partial current density， validating the positive correlation between in-situ-engineered defects and NO3RR performance. Importantly， the defect-rich dual-phase structure enabled long-term stability， maintaining ~90% NH3 Faradaic efficiency at 1.5 A/cm2 for 300 h， highlighting the significance of in-situ dynamic defect regulation in boosting both reactivity and durability of Cu-based catalysts for NO3RR.
An alternative strategy to enhance NO3RR demonstrated by oxide-derived copper nanosheet arrays （OD-Cu NSs） with residual lattice oxygen and tensile strains （Fig.9h~i）[43]. Operando spectroscopic and DFT evidence revealed that the in-situ electrochemical reduction of CuO nanosheets produced OD-Cu nanosheets with residual lattice oxygen （~10 at%） and significant tensile strains （~2.9%）， which collectively addressed two major challenges in Cu-based NO3RR of *NO2 poisoning and *H scarcity. The residual lattice oxygen withdrew surface electron density， reducing NO2 adsorption （-1.28 eV vs -1.62 eV on pristine Cu） and promoting its hydrogenation， while tensile strains increased the Cu d-band center and lowered the water dissociation barrier （ΔGH2O = 0.11 eV vs 0.32 eV）， ensuring a sufficient supply of *H for hydrogenation. These dynamically generated defects enabled OD-Cu to achieve a 99.7% NH3 Faradaic efficiency， a yield of 6.20 mmol/（h·cm2） at 1.5 A/cm2， and 100 h of durability. This demonstrated that defect engineering through in-situ lattice-oxygen/strain coupling was a powerful approach to regulate catalytic properties of Cu-based materials for efficient NO3RR.

5 Challenges and perspectives

While significant progress has been made in understanding and regulating the dynamic evolution of copper-based catalysts for the electrochemical NO3​RR， several fundamental challenges persist. These barriers must be overcome to move beyond laboratory-scale research and toward practical， industrial applications. The challenges are not merely technical but also involve a deeper mechanistic understanding and a shift in research paradigms.
Mechanistic ambiguity of active sites： One of the most profound challenges in NO3​RR catalysis is the lack of a clear and universal understanding of the true active sites and the precise reaction mechanism. The process is inherently complex， involving multiple proton-coupled electron transfer steps and a variety of potential intermediates （NO2-， NO， N2O， etc.）. The dynamic nature of copper catalysts complicates this further， as the initial catalyst structure may not be the one responsible for the observed high activity. The active site is a “living” entity， constantly evolving under reaction conditions. Researchers grapple with questions such as： Is the active site a specific crystal facet， a surface defect， an edge site， or is it a specific copper oxidation state， such as Cu+ or Cu0， or a synergy between them? Furthermore， the role of co-catalysts or support materials in creating specific interfaces that facilitate the reaction remains largely speculative. This mechanistic ambiguity makes rational catalyst design a trial-and-error process， as we are often optimizing a phenomenon without a complete understanding of its fundamental drivers.
Limited monitoring capabilities： Our ability to directly observe and track the dynamic evolution of catalysts in real-time is still limited. Traditional characterization methods are largely ex situ， meaning they analyze the catalyst before and after the reaction. This approach provides a static “snapshot” and fails to capture the intricate， minute-to-minute changes that occur under operating conditions. While the development of operando techniques， such as in-situ XAS， Raman spectroscopy， and electron microscopy， has been a major breakthrough， they face their own challenges. These techniques are often costly， require specialized facilities （like synchrotrons）， and can be technically difficult to implement under realistic electrochemical conditions. Moreover， the resolution of these techniques may not be sufficient to capture fast， localized changes on the catalyst surface. There is a critical need for new， more accessible， and higher-resolution operando tools that can provide a comprehensive picture of structural， chemical， and electronic transformations as they happen.
Trade-offs between activity and stability： A recurring theme in catalysis is the inherent trade-off between high activity and long-term stability. The dynamic evolution that generates highly active， reconstructed surfaces is often the same process that leads to catalyst deactivation over time. For example， the formation of highly reactive surface sites may also lead to the dissolution of Cu or the aggregation of nanoparticles， which reduces the effective surface area and diminishes performance. The catalyst that shows exceptional activity in short-term tests may quickly lose its efficiency after prolonged operation. This presents a significant design challenge： how can a catalyst be flexible enough to evolve into a highly active state while being robust enough to maintain that state for thousands of hours of continuous operation? The current strategies for enhancing stability often involve stabilizing the catalyst to prevent restructuring， which can inadvertently hinder the formation of the most active evolved structures.
Scalability Barriers： Moving from the carefully controlled environment of the laboratory to a full-scale industrial process introduces a new set of challenges related to scalability. First， the synthesis of advanced nanostructured catalysts with precise morphological and defect control is difficult to reproduce consistently at a large scale. The delicate processes that yield highly effective catalysts in milligram quantities are often not easily transferable to kilogram-scale production. Second， mass transport limitations become much more pronounced in larger reactors. Efficient delivery of nitrate ions to the electrode surface and removal of ammonia from the system are crucial for maintaining high performance， but are significantly more complex in a large-scale flow system than in a stirred batch reactor. Finally， economic viability must be considered. While a catalyst may show high Faradaic efficiency in the lab， its cost， durability， and the overall energy consumption of the system must be optimized for it to be a commercially viable technology for ammonia production.
To truly unlock the potential of copper-based catalysts for sustainable NO3​RR and to overcome the challenges outlined above， the field must pursue several key research directions. A holistic approach that integrates advanced characterization， rational design， and large-scale system optimization will be essential.
Advanced in-situ characterizations： The ability to observe dynamic catalyst evolution as it happens is paramount. While operando techniques have provided a window into these processes， future research must focus on developing and integrating advanced multi-modal methods. This involves combining techniques that offer complementary information. For instance， coupling in-situ XAS and XRD can provide simultaneous insights into both the atomic-scale electronic structure and the long-range crystalline order， respectively. Similarly， combining an electrochemical quartz crystal microbalance with Raman or infrared spectroscopy could help monitor mass changes and surface species adsorption in real-time. The ultimate goal is to move beyond simple before-and-after analysis and to set a “catalytic movie” that captures the full spectrum of dynamic transformations， from initial restructuring to the final steady-state of the active phase. Such data is crucial for validating theoretical models and guiding the rational design of new catalysts.
Intelligent pre-catalyst design： The current approach to catalyst synthesis is often empirical. Future efforts should shift towards an “intelligent” design paradigm， where the pre-catalyst is intentionally engineered to evolve into a desired active structure under reaction conditions. This can be achieved through the precise control of initial parameters， such as precursor composition， morphology， and crystallographic orientation. By understanding the thermodynamic and kinetic driving forces behind dynamic evolution， researchers can synthesize pre-catalysts that follow a "programmed" pathway， predictably forming and stabilizing the most active configurations. For example， the catalyst precursor could be designed with specific defects or interfaces that act as nucleation sites for the formation of a highly active Cu0 phase， or a stable Cu2​O layer， during the initial minutes of the reaction. This approach minimizes the guesswork and allows for a more efficient and targeted development process.
Theoretical-experimental synergy： The future of catalyst research lies in a tight-knit synergy between theoretical modeling and experimental work. High-throughput DFT calculations and machine learning algorithms can serve as powerful predictive tools. These computational methods can rapidly screen thousands of potential catalyst structures， predict their evolution under various potentials， and identify promising active sites and reaction pathways. Theoretical models can pinpoint specific intermediates or surface states that are energetically favorable for ammonia formation， guiding experimentalists to synthesize catalysts that are more likely to achieve the desired outcome. The experimental results， in turn， can be used to refine and validate the theoretical models， creating a continuous feedback loop that accelerates discovery. This synergy will be critical for overcoming the mechanistic ambiguity challenge and for rationally designing catalysts with enhanced activity and selectivity.
Stability enhancement engineering： Addressing the trade-off between activity and stability is a critical long-term goal. Instead of simply stabilizing the initial catalyst structure， future research must focus on mechanisms that enhance the durability of the dynamically evolved active phase itself. This could involve exploring strong metal-support interactions to physically anchor the copper nanoparticles， preventing aggregation and dissolution. The use of heteroatom doping （e.g.， with nitrogen or carbon） or the creation of stable interfaces with a support material can also act as “structural anchors”， suppressing undesirable restructuring while allowing for the formation of the desired active sites. Furthermore， designing robust electrode architectures that improve mechanical stability and facilitate efficient mass transport will be crucial for maintaining performance over extended periods.

6 Conclusions

This review has comprehensively explored the intricate role of dynamic evolution in Cu-based catalysts for the NO3RR， highlighting that the active surface is rarely the one initially synthesized but rather a reconstructed structure formed in-situ under reaction conditions. We have systematically analyzed the mechanisms driving this evolution， from the restructuring of crystal facets and the formation of new active phases to the creation of beneficial heterogeneous interfaces. The discussion also highlighted the critical importance of advanced operando techniques， which have been instrumental in providing real-time insights into these transformations， bridging the gap between static structural analysis and dynamic electrochemical performance.
Furthermore， we detailed a spectrum of modern strategies for regulating this dynamic nature， including precise control over the valence state， morphological engineering， and defect creation. These approaches demonstrate that catalyst evolution can be a controlled， designable process rather than an uncontrolled phenomenon. However， as the field progresses， significant challenges remain， particularly in resolving the mechanistic ambiguity of active sites， overcoming the inherent trade-offs between activity and long-term stability， and scaling up these complex systems for practical application.
Ultimately， the future of NO3​RR catalysis hinges on a new paradigm of intelligent catalyst design. Instead of designing catalysts and hoping for the best， the goal is to rationally engineer pre-catalysts that are “programmed” to evolve into a desired， highly active， and stable configuration under operating conditions. By fostering a strong synergy between advanced theoretical modeling and cutting-edge experimental characterization， we can accelerate the discovery of next-generation NO3RR catalysts. The ability to control and direct dynamic evolution is not just a research objective； it is pivotal for advancing catalyst development toward a sustainable and scalable technology for decentralized ammonia production.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]

Zhang Q, Tao R Z, Huang Y, Tan H Z, Cui B C. Prog. Chem., 2024, 36(5):783.
[2]

(张倩, 陶润泽, 黄宇, 谭厚章, 崔保崇. 化学进展, 2024, 36(5): 783.)
[3]

Lin C Z, Zhu J W, Li W J, Chen H, Feng J T, Yan W. Prog. Chem., 2024, 36(9): 1291.
[4]

(林长征, 朱金薇, 李潍嘉, 陈浩, 冯江涛, 延卫. 化学进展, 2024, 36(9): 1291.)
[5]

Xiong Y C, Wang Y H, Zhou J W, Liu F, Hao F K, Fan Z X. Adv. Mater., 2024, 36(17): 2304021.
[6]

Hu T, Wang C, Wang M, Li C M, Guo C. ACS Catalysis, 2021, 11: 14417.
[7]

Chen F Y, Elgazzar A, Pecaut S, Qiu C, Feng Y G, Ashokkumar S, Yu Z, Sellers C, Hao S Y, Zhu P, Wang H T. Nat. Catal., 2024, 7(9): 1032.
[8]

Liu S Y, Wei Y K, Tan Y, Yuan W M, Liang K X, Zhang S H. Prog. Chem., 2024, 36(8):1134.
[9]

(刘锶宇, 韦一可, 檀玉, 苑维铭, 梁可心, 张胜寒. 化学进展, 2024, 36(8):1134.)
[10]

Wu Y S, Jiang Z, Lin Z C, Liang Y Y, Wang H L. Nat. Sustain., 2021, 4(8): 725.
[11]

Li J, Zhan G M, Yang J H, Quan F J, Mao C L, Liu Y, Wang B, Lei F C, Li L J, Chan A W M, Xu L P, Shi Y B, Du Y, Hao W C, Wong P K, Wang J F, Dou S X, Zhang L Z, Yu J C. J. Am. Chem. Soc., 2020, 142(15): 7036.
[12]

Fang L, Wang S, Song C, Yang X, Li Y, Liu H. J. Hazard. Mater., 2022, 421: 126628.
[13]

Sun H, Lu S, Qi Y Y, Wu J H, Liu T, Qin J S, Rao H. Adv. Sci., 2025: e09323.
[14]

Simonov A N, Koper M T M, MacFarlane D R. Nat. Chem. Eng., 2025, 2(7): 398.
[15]

Garcia-Segura S, Lanzarini-Lopes M, Hristovski K, Westerhoff P. Appl. Catal. B Environ., 2018, 236: 546.
[16]

Zhang S, Wu J H, Zheng M T, Jin X, Shen Z H, Li Z H, Wang Y J, Wang Q, Wang X B, Wei H, Zhang J W, Wang P, Zhang S Q, Yu L Y, Dong L F, Zhu Q S, Zhang H G, Lu J. Nat. Commun., 2023, 14: 3634.
[17]

Liang X Y, Zhu H D, Yang X X, Xue S S, Liang Z Z, Ren X F, Liu A M, Wu G. Small Struct., 2023, 4(6): 2200202.
[18]

Teng M J, Ye J R, Wan C, He G Y, Chen H Q. Ind. Eng. Chem. Res., 2022, 61: 14731.
[19]

Zhang K, Liu Y, Pan Z, Xia Q, Huo X, Esan O C, Zhang X, An L. EES Catalysis, 2024, 2: 727.
[20]

Wang C, Luo Y, Liu J, Zhu R, Zhao P, Fu L, Vela J, Fan H J S. Inorg. Chem., 2025, 64: 16277.
[21]

Lu S. Precision Chemistry, 2025, 3(10): 570.
[22]

Long X H, Zhong T, Huang F, Li P, Zhao H N, Fang J Y, Shu D, He C. Appl. Catal. B Environ. Energy, 2025, 365: 124944.
[23]

Liu X, Xiang T, Liang Y, Zhou X, Wang Z, Liu J, Cheng M, Zhou C, Yang Y. J. Mater. Chem. A, 2024, 12: 26367.
[24]

Wang X J, Yin B, Jiang L R, Yang C, Liu Y, Zou G, Chen S, Zhu M Z. Science, 2023, 381(6659): 784.
[25]

Bae S E, Stewart K L, Gewirth A A. J. Am. Chem. Soc., 2007, 129(33): 10171.
[26]

Wu L, Feng J, Zhang L, Jia S, Song X, Zhu Q, Kang X, Xing X, Sun X, Han B. Angew. Chem. Int. Ed., 2023, 62: e202307952.
[27]

Wen W D, Yan P, Sun W P, Zhou Y T, Yu X Y. Adv. Funct. Mater., 2023, 33(6): 2212236.
[28]

Lim J, Cullen D A, Stavitski E, Lee S W, Hatzell M C. ACS Energy Lett., 2023, 8(11): 4746.
[29]

Cheng X F, He J H, Ji H Q, Zhang H Y, Cao Q, Sun W J, Yan C L, Lu J M. Adv. Mater., 2022, 34: 2205767.
[30]

Xu Y T, Xie M Y, Zhong H Q, Cao Y. ACS Catal., 2022, 12(14): 8698.
[31]

Yuan L L, Zhang Q, Yu L N, Wu Y N, Wang C Y, Shao C Y, Lu S. Inorg. Chem., 2024, 63(35): 16177.
[32]

Feng J X, Wang X S, Pan H. Adv. Mater., 2024, 36(50): 2411688.
[33]

Selvam N C S, Du L J, Xia B Y, Yoo P J, You B. Adv. Funct. Mater., 2021, 31(12): 2008190.
[34]

Bodiuzzaman M, Murugesan K, Yuan P, Maity B, Sagadevan A, Malenahalli H N, Wang S, Maity P, Alotaibi M F, Jiang D-E, Abulikemu M, Mohammed O F, Cavallo L, Rueping M, Bakr O M. J. Am. Chem. Soc., 2024, 146(39): 26994.
[35]

Ding W, Yan K P, Li Y, Hu C Y, Yi H L, Liu C, Wang C F, Zhou C Z, Ma D. J. Environ. Chem. Eng., 2024, 12(6): 114694.
[36]

Zheng M Y, Wan Y C, Yang L P, Ao S, Fu W Y, Zhang Z J, Huang Z H, Ling T, Kang F Y, Lv R T. J. Energy Chem., 2025, 100: 106.
[37]

Li C J, Liu S L, Xu Y, Ren T L, Guo Y N, Wang Z Q, Li X N, Wang L, Wang H J. Nanoscale, 2022, 14(34): 12332.
[38]

Majhi K C, Le Q, Wu Z, Xu X, Jin Y, Shao S, Liu S, Qi Z, Huang J, Sit P H L, Lam J C H. Energy & Fuels, 2025, 39(33):15903.
[39]

Herzog A, Lopez Luna M, Jeon H S, Rettenmaier C, Grosse P, Bergmann A, Roldan Cuenya B. Nat. Commun., 2024, 15: 3986.
[40]

Sun Y, Wu J, Zhang Z, Liao Q L, Zhang S C, Wang X, Xie Y, Ma K K, Kang Z, Zhang Y. Energy Environ. Sci., 2022, 15(2): 633.
[41]

Zhou X, Xu W C, Liang Y Q, Jiang H, Li Z Y, Wu S L, Gao Z H, Cui Z D, Zhu S L. ACS Catal., 2024, 14(16): 12251.
[42]

Márquez V, Fereidooni M, Santos J S, Praserthdam S, Tarek M, Shaikh R R, Khan R A, Praserthdam P. Electrochim. Acta, 2025, 512: 145531.
[43]

Qian S R, Feng K, Li Z W, Chen Y X, Sun X H, Wang Y J, Yan B H, Cheng Y. ACS Catal., 2023, 13(21): 13931.
[44]

Zhang Z Q, Li W H, Zheng C L, Chen K Y, Pang H L, Shi W X, Lu J S. J. Environ. Sci., 2025, 149: 221.
[45]

Duan Y J, Yang P Z, Zhang S, Song Q N, Li Y M, Liu X, Li M. J. Mater. Chem. A, 2025, 13(36):30280.
[46]

Liu J W, Xu Y F, Duan R H, Zhang M S, Hu Y, Chen M X, Han B, Dong J F, Lee C, Kumara L S R, Seo O, Tseng J, Watanabe T, Liu Z, Zhu Q, Xu J W, Ng M F, Wu D S, Yan Q Y. Nat. Commun., 2025, 16: 3595.
[47]

Butcher D P, Gewirth A A. Nano Energy, 2016, 29: 457.
[48]

Wu Q, Shao D S, Dai C C, Wang J R, Li X N, Song P F, Xie W, Xi S B, Zhang L C, Lin X, Luo S Z, Sun S R, An L, Xi P X, Xu Z J. J. Am. Chem. Soc., 2025, 147(33): 30401.
[49]

Ma Y B, Guo L, Chang L, Guo W H, Zhou T, Hao F K, Su W D, Zhou J W, Wang G Z, Shao M Z, Yu J H, Yin J W, Wang Y H, Liu F, Zhang A, Qian K, Wang J, Zhang X Y, Zhou W J, Chu S Q, Ling C Y, Gan L, Guo Z X, Fan Z X. Nat. Commun., 2025, 16: 7632.
[50]

Hu T, Wang M T, Ren L J, Li C M, Guo C X. J. Phys. Chem. Lett., 2024, 15(12): 3258.
[51]

Zuo M Q, Kong Y X, Zhou H, Chen Y P, Sun Y Y, Li S, Han L. Adv. Funct. Mater., 2025: e13364.
[52]

Li S L, Ma P J, Gao C L, Liu L J, Wang X L, Shakouri M, Chernikov R, Wang K W, Liu D M, Ma R G, Wang J C. Energy Environ. Sci., 2022, 15(7): 3004.
[53]

Yang J, Qi H F, Li A Q, Liu X Y, Yang X F, Zhang S X, Zhao Q, Jiang Q K, Su Y, Zhang L L, Li J F, Tian Z Q, Liu W, Wang A Q, Zhang T. J. Am. Chem. Soc., 2022, 144(27): 12062.
[54]

Murphy E, Sun B Y, Rüscher M, Liu Y C, Zang W J, Guo S Y, Chen Y-H, Hejral U, Huang Y, Ly A, Zenyuk I V, Pan X Q, Timoshenko J, Cuenya B R, Spoerke E D, Atanassov P. Adv. Mater., 2024, 36(27): 2401133.
[55]

Huang H, Zhang Y C, Chen W J, Chen J L, Zou X H, Lv J J, Chen X Q, Shen Z F, Ge Z G, Guo L H, Yao Y G, Wang Y G. Adv. Energy Mater., 2025, 15(17): 2405534.
[56]

Luo W J, Guo Z Y, Ye L, Wu S L, Jiang Y Y, Xu P, Wang H, Qian J J, Zhou X M, Tang H, Ge Y J, Guan J, Yang Z, Nie H G. Small, 2024, 20(30): 2311336.
[57]

Chen R, Shen S, Wang K, Wang J, Yang W, Li X, Li J, Dong F. Proc. Natl. Acad. Sci. U.S.A., 2023, 120: e2312550120.
[58]

Fang L, Wang S, Lu S, Yin F J, Dai Y J, Chang L, Liu H. Chin. Chem. Lett., 2024, 35(4): 108864.
[59]

Chen Q Y, Deng L Y, Zhang J R, Zhang Y, Zhang L, Lu S, Wang Y W. Materials, 2025, 18(13): 3019.
[60]

Li M, Yang S X, Liu Y F, Zhou Y N, Liu H J, Zhang X Y, Dong B, Tang Q W. J. Colloid Interface Sci., 2025, 700: 138444.
[61]

Jiang Q, Gu M, Pei S, Wang T, Liu F, Yang X, Zhang D, Wu Z, Wang Y, Wei L, Li H. J. Am. Chem. Soc., 2025, 147: 26029.
[62]

Zhang K E, Xu Y F, Liu F T, Wang Q, Zou X H, Tang M C, Leung M K H, Ao Z M, Zhao X H, Zhang X, An L. Adv. Sci., 2025, 12(4): 2410763.
[63]

Paliwal A, Bandas C D, Thornburg E S, Haasch R T, Gewirth A A. ACS Catal., 2023, 13(10): 6754.
[64]

Zhang J, Quast T, Eid B, Chen Y T, Zerdoumi R, Dieckhöfer S, Junqueira J R C, Seisel S, Schuhmann W. Nat. Commun., 2024, 15: 8583.
[65]

Li Y X, Bai Y J, Wang Y W, Lu S, Fang L. Environ. Res., 2025, 266: 120422.
[66]

Wang Y T, Zhou W, Jia R R, Yu Y F, Zhang B. Angew. Chem. Int. Ed., 2020, 59(13): 5350.
[67]

Ye C Y, Yang Y, Wu X X, Ding P, Luo J L, Fu X Z. Prog. Chem., 2022, 34(9):1896.
[68]

(叶淳懿, 杨洋, 邬学贤, 丁萍, 骆静利, 符显珠. 化学进展, 2022, 34(9): 1896.)
[69]

Huang C, Triana C A, Jia B Q, Erni R, Zhao H, Patzke G R. ACS Catal., 2025, 15(13): 11407.
[70]

Liu Z X, Shen F, Shi L, Tong Q W, Tang M E, Li Y M, Peng M, Jiao Z J, Jiang Y, Ao L, Fu W Y, Lv X S, Jiang G M, Hou L A. Environ. Sci. Technol., 2023, 57(27): 10117.
[71]

Zhu Y Q, Wen K Y, Wan X Q, Dong G G, Niu J F. Chin. Chemical Lett., 2025, 36(6): 110399.
[72]

Zhang J G, Huang L R, Tjiu W W, Wu C, Zhang M S, Bin Dolmanan S, Wang S B, Wang M, Xi S B, Aabdin Z, Lum Y. J. Am. Chem. Soc., 2024, 146(44): 30708.
[73]

Dai L Z, Yong G Y, Gan W N, Li T, Chen Z C, Pan L Y, Luo H Q, Li N B. Chem. Eng. J., 2025, 521: 166680.
[74]

Yoon A, Bai L C, Yang F L, Franco F, Zhan C, Rüscher M, Timoshenko J, Pratsch C, Werner S, Jeon H S, de Oliveira Monteiro M C, Chee S W, Roldan Cuenya B. Nat. Mater., 2025, 24(5): 762.
[75]

Manna B K, Khairwal T, Samanta R, Barman S. ACS Appl. Energy Mater., 2025, 8(14): 10695.
[76]

Chen X T, Zhang T, Kan M, Song D G, Jia J P, Zhao Y X, Qian X F. Environ. Sci. Technol., 2020, 54(20): 13344.
[77]

Zhou N, Wang J Z, Zhang N, Wang Z, Wang H G, Huang G, Bao D, Zhong H X, Zhang X B. Chin. J. Catal., 2023, 50: 324.
[78]

Daiyan R, Tran-Phu T, Kumar P, Iputera K, Tong Z Z, Leverett J, Khan M H A, Asghar Esmailpour A, Jalili A, Lim M, Tricoli A, Liu R S, Lu X Y, Lovell E, Amal R. Energy Environ. Sci., 2021, 14(6): 3588.
[79]

Zhou N, Wang Z, Zhang N, Bao D, Zhong H X, Zhang X B. ACS Catal., 2023, 13(11): 7529.
[80]

Sun C Z, Xiao Y G, Liu X, Hu J, Zhao Q, Yin Z L, Cao S S. Inorg. Chem., 2024, 63(25): 11852.
[81]

Yang K W, Han S H, Cheng C Q, Guo C Y, Li T L, Yu Y F. J. Am. Chem. Soc., 2024, 146(19): 12976.
[82]

Jang W, Oh D, Lee J, Kim J, Matthews J E, Kim H, Lee S W, Lee S, Xu Y, Yu J M, Hwang S W, Jaramillo T F, Jang J W, Cho S. J. Am. Chem. Soc., 2024, 146(40): 27417.
[83]

Cao H Y, Zhang X D, Tang M J, Dong W F, Shi W B. Sens. Actuat. B Chem., 2023, 392: 134066.
[84]

He W H, Zhang J, Dieckhöfer S, Varhade S, Brix A C, Lielpetere A, Seisel S, Junqueira J R C, Schuhmann W. Nat. Commun., 2022, 13: 1129.
[85]

Bai L C, Franco F, Timoshenko J, Rettenmaier C, Scholten F, Jeon H S, Yoon A, Rüscher M, Herzog A, Haase F T, Kühl S, Chee S W, Bergmann A, Beatriz R C. J. Am. Chem. Soc., 2024, 146(14): 9665.
[86]

Li Z, Wang L L, Cai Y M, Zhang J R, Zhu W L. J. Hazard. Mater., 2022, 440: 129828.
[87]

Cheon S, Kim B, Kim H W, Kim D, Han J I. ChemSusChem, 2025, 18(6): e202401978.
[88]

Anastasiadou D, van Beek Y, Chen W, Wissink T, Parastaev A, Hensen E J M, Costa Figueiredo M. ChemCatChem, 2023, 15(10): e202201503.
[89]

Shen Z, Xu F F, Cheng X Y, Jiang J T, Zhou C K, U Zeng Y, Wang X Z, Yang L J, Wu Q, Hu Z. ACS Nano, 2025, 19(4): 4611.
[90]

Yu Z, Xie J W, Ren T L, Yu H J, Deng K, Wang Z Q, Wang H J, Wang L, Xu Y. Inorg. Chem., 2023, 62(39): 16228.
[91]

Zhang Y Z, Chen X, Wang W L, Yin L F, Crittenden J C. Appl. Catal. B Environ., 2022, 310: 121346.
[92]

Wu K M, Sun C C, Wang Z N, Song Q, Bai X X, Yu X, Li Q, Wang Z, Zhang H, Zhang J, Tong X, Liang Y P, Khosla A, Zhao Z H. ACS Mater. Lett., 2022, 4(4): 650.
[93]

Guo X T, Gao R T, Gao Z H, Wang L. J. Energy Chem., 2025, 106: 351.
[94]

Hu Q, Qin Y J, Wang X D, Wang Z Y, Huang X W, Zheng H J, Gao K R, Yang H P, Zhang P X, Shao M H, He C X. Energy Environ. Sci., 2021, 14(9): 4989.
[95]

Fu Y F, Wang S, Wang Y, Wei P F, Shao J Q, Liu T F, Wang G X, Bao X H. Angew. Chem. Int. Ed., 2023, 62(26): e202303327.
[96]

Zhang M L, Peramaiah K, Yi M Y, Huang H. J. Mater. Chem. A, 2025, 13(32): 26797.
[97]

Zhang J C, Xing C Y, An S N, Meng F S, Liu Z N, Ge R X, Ma M, Ren J L, Tian J. Green Chem., 2025, 27(24): 7129.
[98]

Li D, Wang F, Mao J. Inorg. Chem., 2023, 62(40): 16283.
[99]

Ding L, Lan H S, Li X, Zhao Z H, Qu Y S, Xia Y J, Chang X H. J. Colloid Interface Sci., 2025, 690: 137318.
[100]

Ren T L, Yu Z, Yu H J, Deng K, Wang Z Q, Li X N, Wang H J, Wang L, Xu Y. Appl. Catal. B Environ., 2022, 318: 121805.
[101]

Li J, Jiang Q L, Xing X J, Sun C L, Wang Y, Wu Z J, Xiong W, Li H. Adv. Sci., 2024, 11(38): 2404194.
[102]

Xia Y J, Li X, Qu Y S, Zhou Y, Weng Z Y, Jin S M, Wang J, Chang X H. J. Mater. Chem. A, 2025, 13(28): 23035.
[103]

Liu X N, Wang Y C, Hu Z M, Yang B W, Smith R L, Su Y Q, Qi X H. Appl. Catal. B Environ. Energy, 2025, 371: 125254.
[104]

Zhang B C, Dai Z C, Chen Y X, Cheng M Y, Zhang H K, Feng P Y, Ke B Q, Zhang Y Y, Zhang G Q. Nat. Commun., 2024, 15: 2816.
[105]

Shi Y, Li Y M, Li R J, Zhao X G, Yu Y L, Yang M. Chem. Eng. J., 2024, 479: 147574.
[106]

Fang L, Wang S, Song C, Lu S, Yang X H, Qi X Q, Liu H. Chem. Eng. J., 2022, 446: 137341.
[107]

Wang Y, Wang S, Fu Y F, Sang J Q, Wei P F, Li R T, Gao D F, Wang G X, Bao X H. Nat. Commun., 2025, 16: 897.

Funding

Henan Province Science and Technology Research and Development Plan Joint Fund （Industry Category， No. 202324111）

Doctoral Scientific Research Foundation of Pingdingshan University(PXY-BSQD2023044)

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