A central challenge in computational catalysis is the identification of low-energy and chemically plausible adsorption configurations, as these directly affect adsorption energies, reaction pathways, and catalytic performance. Existing approaches generally rely on enumerating candidate adsorption sites followed by iterative refinement through density functional theory calculations or machine-learning-based relaxations. However, such workflows remain computationally expensive and are difficult to scale to complex surfaces or multi-adsorbate systems. Here, we introduce Meta-LegNet, a graph learning framework that combines SE(3)-equivariant atom-level message passing with voxel-based multiscale aggregation and cross-domain meta-learning to learn transferable representations of local adsorption environments across diverse catalyst–adsorbate systems. Rather than following a conventional regression-only paradigm, Meta-LegNet encodes local chemical environments using invariant radial features and equivariant directional information, and further incorporates broader structural context through coordinate-frame voxel pooling, assignment-based upsampling, and gated feature fusion. The resulting local-global decomposition produces atom-resolved attribution maps, which are processed to identify adsorption-relevant local environments in an interpretable manner. Based on the learned representations, we further construct an adsorption-environment database and develop a template-matching strategy to propose likely adsorption sites on previously unexplored surfaces without exhaustive site enumeration. Overall, our results suggest that learning transferable adsorption environments provides an accurate, interpretable, and practical route for accelerating catalyst screening.

Although copper is one of the most common components in catalysts for CO2 conversion into valuable C2+ chemicals, a clear and systematic mechanistic explanation of its unique properties in these processes is lacking. Herein, we address this challenge by introducing a novel ansatz to rationally construct catalytic reaction networks and account for realistic active sites on catalyst nanoparticles, applied here to CO2 hydrogenation into C1 and C2 oxygenates, explaining the formation of frequently observed reaction intermediates. We also provide a comparative mechanistic analysis of CO2 hydrogenation on a stronger-adsorbing metal widely used in hydrogenation reactions, Pd. Furthermore, we present a refined approach to Br & oslash;nsted-Evans-Polanyi relationships tailored to structural characteristics of transition states. Our approach facilitates further exploration of CO2 hydrogenation on transition metal-based catalysts, deepening our understanding of the underlying reaction mechanism. This theoretical framework not only elucidates the intricate kinetics of CO2 hydrogenation but also establishes a versatile foundation for rational catalyst design across catalytic domains. This study highlights the unique activity of Cu in various hydrogenation and C-C coupling steps. Unlike scarce metals that require resource-intensive extraction, Cu reduces the environmental impact and costs of CO2 conversion technologies. Clarifying its unique role in converting CO2 into valuable C2+ chemicals moves us closer to balancing economic growth and environmental protection.

Coupled ion-electron interfacial reactivities on electroactive particles are complex and crucial to various battery chemistries and dynamics, yet direct visualization of these reactions remains elusive despite advances in operando imaging. Here we report ion-localization optical nanoscopy (ION) with single-ion, subparticle resolution that distinguishes microscopic static and dynamic disorder in ion-generation interfacial reactivity, offering nondestructive, real-time, non-equilibrium insights. We uncover diverse stripping dynamics of zinc anodes, revealing unexpected subparticle-level heterogeneity and challenging conventional views of uniform stripping on (002)-textured zinc. Mesoscale functional descriptors-intraparticle diffusive and electronic coupling strengths-that govern overall stripping uniformity are identified by ION, supported by computational methods and validated by in situ single-particle manipulation. Imaging-derived insights are further translated into ensemble-level strategies enabling exceptional anode reversibility. ION is cost-effective, high-throughput and broadly applicable to myriad ion-participated interfacial processes, including cathode (de)intercalation, solid-electrolyte interphase evolution, ion exchange and catalyst restructuring.

Methane and methanol can serve as CO2-neutral energy carriers if they are synthesized through CO or CO2 hydrogenation with green hydrogen produced using renewable energy sources. In particular, COx hydrogenation into methanol is conducted on a 100-megaton scale using Cu-ZnO catalysts. The economic attractiveness of COx hydrogenation can be improved by designing more efficient transition-metal catalysts through atomic-scale understanding of the critical effects of nanostructuring and nanoparticle-support interactions on their properties. Herein, we characterize how coordination numbers of metal atoms, nanoconfinement, and nanoparticle interactions with pristine ZnO support (i.e., nanoeffects) affect computational catalyst design for CO hydrogenation into methanol and methane on transition metals, such as Ir, Pd, Pt, Cu, Ag, and Au. The nanoeffects are found to induce shifts of up to 0.3 eV in the linear scaling relationships between the binding energies of C-bound species on the catalyst surface. In contrast, the O-bound species exhibit a larger shift of up to 0.9 eV, particularly at metal-surface interfacial sites. The CO hydrogenation activity of unsupported metal nanoparticles and (211) surfaces is predicted to be enhanced compared with M(111) surfaces by as much as six orders of magnitude in the case of Au. For coinage metals, methanol synthesis is further enhanced by more than two orders of magnitude at the nanoparticle-support interface, while CO methanation on ZnO-supported Pt and Pd is accelerated by over four orders of magnitude relative to unsupported nanoparticles. In turn, Ir exhibits a significant enhancement of approximately two orders of magnitude in catalytic activity due to the reduction in coordination number, while the effect of ZnO amplifies its activity by an additional 50 times compared with M(211). As a result, the interfacial Cu/ZnO sites are calculated to have the highest activity for methanol synthesis, whereas the interfacial Ir/ZnO sites exhibit the highest CO methanation rate. This study serves as a proof-of-concept demonstration that nanoeffects, particularly nanoparticle-support interactions, can be leveraged to rationally design more active catalysts for emerging green energy technologies.

MoS2 is a promising catalyst for methanol synthesis from CO2 hydrogenation. It is widely accepted that the in-plane sulfur vacancies are the active sites for methanol formation, while the edge sulfur vacancies catalyze methane formation, which is typically undesirable. Rather than blocking the edge sites with heteroatom dopants, we demonstrate that decorating the edges of MoS2 with functional FeOx clusters effectively boost methanol formation via synergy between the RWGS activity of the anchored FeOx clusters and the CO hydrogenation activity of the in-plane sulfur vacancies. Synthetically, this was achieved by a vacuum impregnation method that chemically anchors FeOx clusters on the edges of MoS2, forming stable Fe-S/Fe-O interfaces. With these Fe-S/Fe-O interfaces, the Fe/MoS2 catalyst shows markedly enhanced methanol selectivity (to 80%) and a high intrinsic space-time-yield (STY) of 0.6 mmolCH3OH·m-2·h-1. In situ spectroscopic and microscopic characterizations combined with theoretical calculations corroborate that the formation of amorphous FeOx clusters at edge sites of MoS2 could effectively suppress edge Sv formation, generate additional CO*, and promote the formation of methanol at the in-plane Sv sites. Broadly speaking, this work has demonstrated the modulation of MoS2 edge sites using simple and scalable catalyst preparation methods to enhance methanol formation.

The development of technologies for CO2 hydrogenation to valuable chemicals is prioritized globally due to their potential for large-scale CO2 abatement. However, rational design of catalysts for this reaction is hindered by an incomplete understanding of the complex reaction network of CO2 hydrogenation. This study addresses this gap by establishing a data- and computation-efficient framework for CO2 hydrogenation to C1-2 oxygenates on ≈ 1.2 nm large Au, fcc-Co, Cu, Ni, Pd, Pt, and Rh nanoparticles. The obtained reaction networks could not be described using Brønsted-Evans-Polanyi relationships with sufficient accuracy, motivating the use of a nonlinear NN model for inference of activation energies. A pathfinder-like algorithm and an energetic span model are used to analyse these networks and to construct Sabatier volcano plots identifying optimal binding properties for a promising catalyst. The analysis indicates that efficient C2 oxygenate formation simultaneously requires CHx formation, favorable C-C coupling and efficient protonation, as well as suppression of methanation and poisoning by CO, alcoholates, and carboxylates. These features are mutually exclusive on the studied monometallic nanoparticles, suggesting that multicomponent and multimetallic catalysts are more realistic design targets. This framework supports computational discovery of CO2 hydrogenation catalysts challenged by data limitations and mechanistic complexity.

The formation of bimetallic alloys enables simultaneous tuning of geometric and electronic structures, offering an avenue to control adsorption and reaction pathways on catalytic surfaces. In this study, we elucidate how Pd-Fe alloy nanoparticles achieve such modulation at the atomic level. Colloidal synthesis ensures homogeneous atomic mixing, allowing the disentanglement of ensemble and ligand effects in CO adsorption and hydrogenation. Herein, we demonstrate that PdFe alloy nanoparticles effectively suppress the sequential hydrogenation of CO to CH4, enabling CO production with near-unity selectivity, an outcome unattainable with monometallic catalysts. Comparative analysis with PdCo alloys reveals that the role of Fe lies in its ability to downshift the Pd d-band center and geometrically isolate Pd sites, leading to weakened CO binding and inhibited C-O bond hydrogenation. These findings highlight the cooperative geometric and electronic effects underlying alloy formation and establish Pd-Fe as a model system for understanding selective adsorption control in catalytic alloys.

The concentration and reactivity of H atoms on oxide surfaces govern the rate and selectivity of many hydrogenation reactions, such as CO2 conversion to methanol. On some oxides, H-2 molecules may dissociate in a heterolytic fashion and populate the surface with chemically distinct proton H+ and hydride H- species. In this study, we demonstrate through systematic density functional theory (DFT) simulations that H-2 dissociates heterolytically on MgO(110), CaO(110), SrO(110), anatase TiO2(101), ZrO2(101), HfO2(001), ZnO (1010), alpha-Al2O3(110), gamma-Al2O3(110), and beta-Ga2O3(100) surfaces. These oxides do not form two H+ species upon H-2 dissociation because their high band gaps are found to hinder the injection of released electrons into the conduction band. In contrast, reduced oxide surfaces such as Ti2O3(012), Ce2O3(0111), and FeO(100) preferentially stabilize H- over H+ upon adsorption of a single H atom. The oxygen vacancy formation energy was found to serve as a general descriptor of the preference for H+ + H- over H+ + H+ as the H-2 dissociation product across all considered oxides. Moreover, we show that the surface H+ species preferentially react with O during CO2 hydrogenation to form COOH intermediates, whereas surface H- species preferentially attack C atoms, forming HCOO intermediates. Thus, tuning the band gap of oxide surfaces is suggested as a strategy to control the charge of surface H atoms, which is shown to direct the hydrogenation pathway and the selectivity of oxide catalysts.

Abstract Alloy nanoparticles (nanoalloys) find applications in many fields including catalysis and green energy technologies. However, the computational design of nanoalloys is hindered by the uncertainty in their arrangement of constituent elements within the particle, i.e. their chemical ordering. Herein, we present a method for realistic simulations of trimetallic alloy nanocrystallites, considering both lowest energy chemical ordering and thermal disorder. This approach uses Monte Carlo simulations based on a topological lattice Hamiltonian, with parameters derived from DFT simulations of carefully designed nanoalloy structures. Using this method, we characterized chemical orderings in nanoparticles composed of 79 and 338 atoms of metals with known catalytic activity in CO2 hydrogenation, namely, Pd-Pt-Cu, Ni-Pd-Cu, and Co-Rh-Cu. Our simulations show that the thermal disorder in these alloys affects the average binding energies of reaction intermediates to the catalyst surface by up to 1.1 eV, implying their critical effect on the alloy’s surface reactivity. We show that the developed method can be used for brute-force evaluation of entropic contributions to mixing free energies in alloy nanoparticles. The proposed method efficiently generates realistic models of trimetallic nanoalloys, enabling reliable simulations of their properties for in-depth understanding and computational design of alloy nanoparticles.

The development of cost-effective and acid-stable oxygen evolution reaction (OER) electrocatalysts is essential for the implementation of a proton exchange membrane-based water electrolyzer. Herein, we develop a catalyst composite featuring RuO2 nanoplates on a manganese-cobalt-spinel oxide (MnCo2O4.5) substrate for an acidic OER. The optimized RuO2/MnCo2O4.5 catalyst composite exhibits a low overpotential of 210 mV at 10 mA cm-2 and remarkable stability with a degradation rate as low as 30 mu V h-1. Density functional calculations reveal that Co/Mn doping in the RuO2 nanoplates significantly enhances their OER activity and stability against Ru dissolution. Additionally, the pH-dependent and kinetic isotope experiments underscore that RuO2/MnCoO4.5 exhibits enhanced OER activity in an acidic electrolyte compared to that in an alkaline electrolyte, highlighting its potential for use in PEM electrolyzers. When integrated into a PEM electrolyzer, RuO2/MnCo2O4.5 achieves an encouragingly low cell voltage of 1.83 V at 1 A cm-2 and operates stably for over 130 h with negligible degradation. Overall, this study provides in-depth insights into OER mechanisms, particularly in mitigating Ru dissolution under acidic conditions, a critical factor for the development of robust and cost-effective OER catalysts.

Mambalgins are peptide inhibitors of acid-sensing ion channels type 1 (ASIC1) with potent analgesic effects in models of inflammatory and neuropathic pain. To optimize recombinant peptide production and enhance pharmacological properties, we developed a mutant analog of mambalgin-1 (Mamb) through molecular modeling and site-directed mutagenesis. The resulting peptide, Mamb-AL, features methionine-to-alanine and methionine-to-leucine substitutions, allowing for a more efficient recombinant production protocol in E. coli. Electrophysiological experiments demonstrated that Mamb-AL exhibits three-fold and five-fold greater inhibition of homomeric ASIC1a and ASIC1b channels, respectively, and a two-fold increase in inhibition of heteromeric ASIC1a/3 channels compared with Mamb. In a mouse model of acetic acid-induced writhing pain, Mamb-AL showed a trend toward stronger analgesic efficacy than the wild-type peptide. These improvements in both production efficiency and pharmacological properties make Mamb-AL a valuable tool for studying ASIC channels and a promising candidate for analgesic drug development.

The reactivity of nanoparticles is governed by their surface composition, which tends to vary significantly under reactive conditions. In this study, the impacts of temperature and the presence of a CO or O2 atmosphere on the structures of Au79, Cu19Au60, Cu39Au40, Cu60Au19, and Cu79 nanoparticles were investigated using a combination of global optimization, machine learning interatomic potentials, and density functional theory. We find that increasing gold content weakens CO and O adsorption and limits oxygen-induced structural changes, while copper-rich particles undergo pronounced oxidation and reconstruction. Bader charge analysis shows that, in bare nanoalloys, Au withdraws electron density from Cu due to their electronegativity difference. In oxidized Cu-Au nanoalloys, the charge on Cu atoms depends on the number of O neighbors and can reach values typical of Cu2O (0.6 e) and CuO (1.1 e). On average, the Cu atoms exhibit positive effective Bader charges of ∼0.9 e in these nanoalloys. Our results indicate that models of Cu-Au catalysts under oxidizing conditions must incorporate the full ensemble of metallic and oxidized configurations rather than a single most probable structure to predict true catalytic activity.

Boron can become unintentionally incorporated into transition metals during the reduction of metal salts with borohydride. The presence of boron at the surfaces of transition metals (TMs) such as Pd and Pt is known to significantly influence their catalytic properties. In this study, we employ density functional (DFT) calculations to investigate the thermodynamics and kinetics of boron incorporation into ∼1.5 nm particles and extended (111) surfaces of fcc-Co, Rh, Ir, Ni, Pd, Pt, Cu, Ag, Au, and Al. Our results reveal that boron exhibits high thermodynamic stability in interstitial subsurface sites on (111) surfaces and nanoparticles of Rh, Pt, and Pd. Unlike extended surfaces, metal nanoparticles (NPs) can also stabilize boron within the coordination environment of surface metal atoms, with such sites being particularly stable in Rh, Ir, and Ni nanoparticles. Furthermore, the energy barriers for B migration at NP edge sites from the surface to subsurface decrease to <0.5 eV (for all metals except Ir), and the migration barrier for boron incorporation into the in-surface sites is lower than 0.2 eV. Notably, B incorporation induces a shift in the d-band center of adjacent metal atoms, which indicates its pronounced impact on the catalytic activity of transition metals.

Acid-sensing ion channel 1a (ASIC1a) is involved in processes associated with fear, learning, and neurodegeneration within the central nervous system. However, ASIC1a is also abundant in the peripheral nervous system, where its role is still poorly understood, largely due to the lack of selective ligands. In this study, we present the discovery of the first selective positive allosteric modulator for ASIC1a, isolated from the sea anemone Metridium senile. The active compound, a peptide named Ms13-1, features a novel type of fold named 'Cys-ladder'. Ms13-1 exhibits high affinity and selectivity for ASIC1a, enhancing channel activation in response to a broad range of acidic stimuli (pH 6.9-5.5) without altering the proton affinity for the channel. Moreover, injection of Ms13-1 into the hind paw of mice provokes robust and long-lasting pain-related behavior, which is significantly attenuated by a selective ASIC1 antagonist. The discovery of this novel selective positive allosteric modulator opens up new perspectives to investigate the role of ASIC1a in various physiological processes.

Acid-sensing ion channels (ASICs), which act as proton-gating sodium channels, have garnered attention as pharmacological targets. ASIC1a isoform, notably prevalent in the central nervous system, plays an important role in synaptic plasticity, anxiety, neurodegeneration, etc. In the peripheral nervous system, ASIC1a shares prominence with ASIC3, the latter well established for its involvement in pain signaling, mechanical sensitivity, and inflammatory hyperalgesia. However, the precise contributions of ASIC1a in peripheral functions necessitate thorough investigation. To dissect the specific roles of ASICs, peptide ligands capable of modulating these channels serve as indispensable tools. Employing molecular modeling, we designed the peptide targeting ASIC1a channel from the sea anemone peptide Ugr9-1, originally targeting ASIC3. This peptide (A23K) retained an inhibitory effect on ASIC3 (IC50 9.39 µM) and exhibited an additional inhibitory effect on ASIC1a (IC50 6.72 µM) in electrophysiological experiments. A crucial interaction between the Lys23 residue of the A23K peptide and the Asp355 residue in the thumb domain of the ASIC1a channel predicted by molecular modeling was confirmed by site-directed mutagenesis of the channel. However, A23K peptide revealed a significant decrease in or loss of analgesic properties when compared to the wild-type Ugr9-1. In summary, using A23K, we show that negative modulation of the ASIC1a channel in the peripheral nervous system can compromise the efficacy of an analgesic drug. These results provide a compelling illustration of the complex balance required when developing peripheral pain treatments targeting ASICs.

Few-layer MoS2 were recently discovered as promising catalyst for CO2 hydrogenation to methanol, despite extreme conditions proposed for its synthesis. Herein, we developed an exceptionally facile, safe, and scalable strategy to prepare single-layer MoS2 (s-MoS2) at ambient pressure using instantaneous self-assembled micelles of didodecyldimethylammonium (DA)-MoS4 complexes as precursor. During the pyrolysis, the presence and subsequent decomposition of coordinated DA inhibited the growth and sheet-stacking of MoS2 in c-direction, resulting in discrete s-MoS2 molecular sheets which maximize the exposure of in-plane S vacancies (Sv). Remarkably, s-MoS2 displayed 77% methanol selectivity and methanol space time yield of 1.54 g center dot g(MoS2)(-1)center dot h(-1), representing the top levels among reported MoS2 catalysts under similar conditions. Density functional theory (DFT) simulations attribute high activity of s-MoS2 to its ability to stabilize in-plane low-coordinated Mo atoms in the vicinity of Sv on both sides of monolayer. The superior performance of s-MoS2 creates new prospects for technological applications beyond CO2 hydrogenation.

Oxidative addition (OA) is a necessary step in mechanisms of widely used synthetic methodologies such as the Heck reaction, cross-coupling reactions, and the Buchwald-Hartwig amination. This study pioneers the exploration of OA of aryl halide to palladium nanoparticles (NPs), a process previously unaddressed in contrast to the activity of well-studied Pd(0) complexes. Employing DFT modeling and semi-empirical metadynamics simulations, the oxidative addition of phenyl bromide to Pd nanoparticles was investigated in detail. Energy profiles of oxidative addition to Pd NPs were analyzed and compared to those involving Pd(0) complexes forming under both ligand-stabilized (phosphines) and ligandless (amine base) conditions. Metadynamics simulations highlighted the edges of the (1 1 1) facets of Pd NPs as the key element of oxidative addition activity. We demonstrate that OA to Pd NPs is not only kinetically facile at ambient temperatures but also thermodynamically favorable. This finding accentuates the necessity of incorporating OA to Pd NPs in future investigations, thus providing a more realistic view of the involved catalytic mechanisms. These results enhance the understanding of aryl halide (cross-)coupling reactions, reinforcing the concept of a catalytic "cocktail". This concept posits dynamic interconversions between diverse active and inactive centers, collectively affecting the outcome of the reaction. High activity of Pd NPs in direct C-X activation paves the way for novel approaches in catalysis, potentially enhancing the field and offering new catalytic pathways to consider.

Valeriana officinalis L. is a widely spread representative of the Eurasian flora. Preparations based on valerian extracts are recognized as sedatives, antidepressants and anxiolytics by both traditional and modern medicine. Despite the extremely high interest in the biological and pharmacological properties of valerian extracts, the composition of biologically active substances and specific activities of their components are still not completely studied. Therefore, here we address the metabolic profiles of the valerian plants with a special emphasis on their major root constituents with pronounced neuroprotective activity. The following phytochemical analysis of the V. officinalis L. roots led to isolation of nine compounds, including two previously undescribed iridoids – isovaltrate isovaleroyloxyisohydrin (1) and isovaltrate α-(isovaleroxy)isovaleroxyhydrin (2) together with one previously undescribed valerenic acid derivative – 2-oxovalerenic acid (3). Three previously described iridoids – iridoidvol A (4), isovaltrate isovaleroyloxyhydrin (5) and valtrate hydrine B1 (6), along with valerenic acid (7) and its metabolites - hydroxyvalerenic (8) and acetoxyvalerenic acids (9) were isolated. Functional assays accomplished with the isolated compounds revealed moderate antioxidant activity characteristic for all of them. Moreover, high neuroprotective potential was observed for the compound 3 (and to less extent of 8). This potential was manifested by a pronounced antineurodegenerative effect in the model of Aβ25-35 induced cytotoxicity. In respect of thrombin-induced platelet aggregation, compounds 1, 4 and 7 demonstrated pronounced inducing and compounds 2, 5 and 6 showed clear potentiating activity. Valerenic acid showed activity on TRPV1 ion channels, manifested by both channel activation and potentiation of the capsaicin response in CHO cells expressing TRPV1. Moderate antioxidant activity was characteristic of all compounds.

Tafalgin (Taf) is a tetrapeptide opioid used in clinical practice in Russia as an analgesic drug for subcutaneous administration as a solution (4 mg/mL; concentration of 9 mM). We found that the acid-sensing ion channels (ASICs) are another molecular target for this molecule. ASICs are proton-gated sodium channels that mediate nociception in the peripheral nervous system and contribute to fear and learning in the central nervous system. Using electrophysiological methods, we demonstrated that Taf could increase the integral current through heterologically expressed ASIC with half-maximal effective concentration values of 0.09 mM and 0.3 mM for rat and human ASIC3, respectively, and 1 mM for ASIC1a. The molecular mechanism of Taf action was shown to be binding to the channel in the resting state and slowing down the rate of desensitization. Taf did not compete for binding sites with both protons and ASIC3 antagonists, such as APETx2 and amiloride (Ami). Moreover, Taf and Ami together caused an unusual synergistic effect, which was manifested itself as the development of a pronounced second desensitizing component. Thus, the ability of Taf to act as a positive allosteric modulator of these channels could potentially cause promiscuous effects in clinical practice. This fact must be considered in patients’ treatment.