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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Respiratory Vinyl Chloride Reductive Dechlorination to Ethene in TceA-Expressing Dehalococcoides mccartyi

Bioremediation of chlorinated ethenes in anoxic aquifers hinges on organohalide-respiring Dehalococcoidia expressing vinyl chloride (VC) reductive dehalogenase (RDase). The tceA gene encoding the trichloroethene-dechlorinating RDase TceA is frequently detected in contaminated groundwater but not recognized as a biomarker for VC detoxification. We demonstrate that tceA-carrying Dehalococcoides mccartyi (Dhc) strains FL2 and 195 grow with VC as an electron acceptor when sufficient vitamin B 12 (B 12 ) is provided. Strain FL2 cultures that received 50 μg L –1 B 12 completely dechlorinated VC to ethene at rates of 14.80 ± 1.30 μM day –1 and attained 1.64 ± 0.11 × 10 8 cells per μmol of VC consumed. Strain 195 attained similar growth yields of 1.80 ± 1.00 × 10 8 cells per μmol of VC consumed, and both strains could be consecutively transferred with VC as the electron acceptor. Proteomic analysis demonstrated TceA expression in VC-grown strain FL2 cultures. Resequencing of the strain FL2 and strain 195 tceA genes identified non-synonymous substitutions, although their consequences for TceA function are currently unknown. The finding that Dhc strains expressing TceA respire VC can explain ethene formation at chlorinated solvent sites, where quantitative polymerase chain reaction analysis indicates that tceA dominates the RDase gene pool.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Adsorption↗
DOE OSTI2025

Clifford Circuit-Based Heuristic Optimization of Fermion-To-Qubit Mappings

Simulation of interacting Fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analyzing Fermionic systems with a quantum computer hinges on the efficiency of Fermion-to-qubit mappings that encode nonlocal Fermionic degrees of freedom in local qubit degrees of freedom. While recent studies have highlighted the importance of designing Fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far either are restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, in this work, we address this challenge by designing a heuristic numerical optimization framework for Fermion-to-qubit mappings. To this end, we first translate the Fermion-to-qubit mapping problem to a Clifford circuit optimization problem and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all Fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between 15% and 40% improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for 6 × 6 nearest-neighbor hopping and Hubbard models by more than 40% and 20%, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperforms any ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific Fermionic Hamiltonian.

Hamiltonians↗
DOE OSTI2025

Nonadiabatic Force Matching for Alchemical Free-Energy Estimation

We propose a method to compute free-energy differences from nonadiabatic alchemical transformations by using flow-based generative models. The method, nonadiabatic force matching, hinges on estimating the dissipation along an alchemical switching process in terms of a nonadiabatic force field that can be learned through stochastic flow matching. The learned field can be used in conjunction with short-time trajectory data to evaluate upper and lower bounds on the alchemical free energy that variationally converge to the exact value if the field is optimal. Applying the method to evaluate the alchemical free energy of atomistic models shows that it can substantially reduce the simulation cost of a free-energy estimate at a negligible loss of accuracy when compared with thermodynamic integration.

Computational chemistry↗
DOE OSTI2023

Development of a Systematic and Extensible Force Field for Peptoids (STEPs)

Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. In conclusion, equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Four-Terminal Electrochemistry: A Back-Gate Controls the Electrochemical Potential of a 2D Working Electrode

We demonstrate that ultrathin semiconductor working electrodes integrated into metal–insulator–semiconductor (MIS) stacks are an enabling platform for understanding non-Faradaic semiconductor electrochemistry. Furthermore, 5 nm thick ZnO electrodes were deposited on 30 nm HfO 2 dielectric on a Pd “gate” electrode. Application of a bias V G between the Pd gate and the ZnO electrode causes electrons to accumulate in the ZnO layer as measured by recording the in-plane sheet conductance. By contacting the top surface of the ZnO layer with the electrolyte in a conventional three-electrode electrochemical cell, we show that the gate voltage V G modulates the electrochemical potential V ZnO of the ZnO film with respect to a reference electrode. Electrochemical potential changes ΔV ZnO up to –1 V vs Ag/Ag + are achieved for V G = +7 V. Furthermore, by measuring V ZnO vs V G , we extract the quantum capacitance CQ of the ZnO film as a function of the Fermi-level position, which provides a direct measure of the ZnO electronic density of states (DOS). Finally, we demonstrate that the gated ZnO working electrodes can disentangle the two principal components of electrochemical potential, namely, the Fermi-level shift Δδ and the double-layer charging energy eΔΦ EDL . This disentanglement hinges on a fundamental difference between back-gating and normal electrochemical control, namely, that electrochemical control requires double-layer charging, while back-gate control does not. Collectively, the results show that the backside gate electrode is an effective fourth terminal that enables measurements that are difficult to achieve in conventional three-terminal electrochemical setups.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessing the Sensitivity of Pourbaix Diagrams to Computational Protocols: Electrochemical Stability of Ni Oxides as a Case Study

Pourbaix diagrams stand as a useful tool in assessing and visualizing materials’ electrochemical stability and are widely used for electrocatalyst design. However, their reliability hinges on the accuracy of the chemical potentials of involved phases, which may bear uncertainties and can be significantly impacted by decision-making steps in the computational protocol. Here, this study introduces a robust sensitivity analysis framework, exemplified through a detailed examination of the computational Pourbaix diagram of Ni, the oxides of which are used as high-activity and cost-friendly catalysts for many electrochemical reactions. Quantities of interest derived from the Pourbaix diagram include the appearance and stability domain of the catalytically active Ni oxide phases along with the onset electrochemical potentials of phase transitions. These metrics can guide the design of operational conditions for Ni oxide electrocatalysts. We find that the employed DFT exchange-correlation functional has the most significant influence on the computed Pourbaix diagram. Uncertainties on crystal structures, along with their related ab initio energetics, are also found to affect the size of the phase stability domain. Higher-order coupling among input parameters is found to play a crucial role in influencing the appearance and distribution of Ni phases in the diagram. Our findings suggest a need to consider variations and uncertainties associated with the computational procedures on predicted Pourbaix diagrams for materials design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Temperature Evolution of the Activation Barriers Leads to Meyer−Neldel Rules for Structural Relaxation and Transport in Polymers

Understanding activation barriers controlling structural relaxation in glass-forming liquids, molecular transport, and ionic conductivity in amorphous polymers is a grand challenge of fundamental scientific and materials engineering interest across disciplines. Over decades, intriguing but puzzling empirical correlations between the elementary time scale of activated barrier crossing and the apparent Arrhenius activation energy, the so-called Meyer−Neldel (MN) rules, have been discovered in diverse liquids and glasses. Here, in this study, we formulate and successfully apply a new experimental analysis and an explicitly dynamical theoretical framework which provides an understanding of the origin, validity, and failure of such correlations, that bridge and unify the three fields of structural relaxation, molecular transport, and ionic conductivity in liquids and quenched glasses. Distinct quasi-universal laws are predicted in equilibrated liquids and nonequilibrium glasses, consistent with experiments. Our analysis reveals that even if the relaxation appears Arrhenius over a limited temperature range, the physical activation barrier is generally temperature-dependent in polymeric systems even below glass transition temperature. In addition, we show that the approximate validity of classical MN rules hinges on a linear temperature dependence of this barrier and the temperature range probed in experiments. Our findings are relevant for controlling the activation barrier in functional soft polymeric materials relevant to molecular separations, barrier coatings, and charge transport, and also provide new constraints on the theoretical understanding of the mechanism underlying slow activated dynamics in glass-forming condensed matter.

Meyer-Neldel rules↗
DOE OSTI2022

Gate-Tunable Transport in Quasi-One-Dimensional α-Bi 4 I 4 Field Effect Transistors

Bi 4 I 4 belongs to a novel family of quasi-one-dimensional (1D) topological insulators (TIs). While its β phase was demonstrated to be a prototypical weak TI, the α phase, long thought to be a trivial insulator, was recently predicted to be a rare higher order TI. Here, we report the first gate tunable transport together with evidence for unconventional band topology in exfoliated α-Bi 4 I 4 field effect transistors. We observe a Dirac-like longitudinal resistance peak and a sign change in the Hall resistance; their temperature dependences suggest competing transport mechanisms: a hole-doped insulating bulk and one or more gate-tunable ambipolar boundary channels. Our combined transport, photoemission, and theoretical results indicate that the gate-tunable channels likely arise from novel gapped side surface states, two-dimensional (2D) TI in the bottommost layer, and/or helical hinge states of the upper layers. Markedly, a gate-tunable supercurrent is observed in an α-Bi 4 I 4 Josephson junction, underscoring the potential of these boundary channels to mediate topological superconductivity.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Rapid Solidification Effects in Additively Manufactured Si and SiGe Compositions

SiGe alloys have a proven track record as robust high-temperature thermoelectric materials, powering NASA missions like SNAP-10A, LES-9, and Voyager 1 and 2. Enhancing thermoelectric efficiency hinges on minimizing thermal conductivity while preserving electrical conductivity. Rapid solidification via LPBF can generate microstructural features such as subgrain cellular boundaries and twinning, which may help reduce thermal conductivity while preserving semiconducting behavior. Here, this study investigates the potential of laser powder bed fusion (LPBF) additive manufacturing to fabricate nanostructured Si and SiGe thermoelectric materials. High cooling rates (10 5 to 10 7 K/s) rates inherent to the LPBF process are conducive to forming such nanostructures. Moreover, this fabrication technique could also be suitable for fabricating complex geometries needed to achieve improved device level performance. Process mapping of commercial Si powder with irregular morphology was first performed to understand the LPBF processing behavior of this semiconductor material. Subsequent studies included in-house synthesized B-doped (p-type) Si7 8 Ge 22 spherical powder that was produced via ultrasonic atomization. Scan strategies involved multiple laser exposures to mitigate solidification cracking: a high density of >98% was achieved, but solidification cracking could not be fully eliminated. Subsequently, a high electrical resistivity (i.e., low conductivity) was observed, but the measured Seebeck coefficient, ∼230 μV/K @ 500 °C, proved that good semiconductor material was being fabricated. A subgrain cellular structure (5–10 μm) was observed as defined by Ge segregation to the intercellular boundaries. The remelting strategies helped lower the cooling rates in processing SiGe, but this still resulted in high residual stresses, which induced a remarkably high density of twins (78–95%) to accommodate the deformation. This unique grain structure offers an avenue for phonon scattering and potential improvements in thermoelectric performance.

figure of merit↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unveiling the Interfacial Reconstruction Mechanism Enabling Stable Growth of the Delafossite PdCoO 2 on Al 2 O 3 and LaAlO 3

Delafossites, composed of noble metal (A + ) and strongly correlated sublayers (BO 2 – ), form natural superlattices with highly anisotropic properties. These properties hold significant promise for various applications, but their exploitation hinges on the successful growth of high-quality thin films on suitable substrates. Unfortunately, the unique lattice geometry of delafossites presents a significant challenge to thin-film fabrication. Different delafossites grow differently, even when deposited on the same substrate, ranging from successful epitaxy to complete growth suppression. These variations often lack a clear correlation to obvious causes like lattice mismatch. Unidentified stabilization mechanisms appear to enable growth in certain cases, allowing these materials to form stable thin films or act as buffer layers for subsequent delafossite growth. This study employs advanced scanning transmission electron microscopy techniques to investigate the nucleation mechanism underlying the stable growth of PdCoO 2 films on Al 2 O 3 and LaAlO 3 substrates grown via molecular-beam epitaxy. Our findings reveal the presence of a secondary phase within the substrate surface that stabilizes the films. This mechanism deviates from the conventional understanding of strain relief mechanisms at oxide heterostructure interfaces and differs significantly from those observed for Cu-based delafossites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Carbonylative Co- and Terpolymerizations of 10-Undecen-1-ol: A Route to Polyketoesters with Tunable Compositions

A strategy to synthesize branched polyketoesters from the carbonylative polymerization of bifunctional α,ω-alkenols such as 10-undecen-1-ol is presented. This strategy hinges on the competitive application of two related catalytic manifolds, alternating alkene/CO copolymerization, and alkene hydroesterification, which share a common metal acyl intermediate. Small molecule model studies of cationic Pd-catalyzed alkene carbonylation in the presence of alcohols demonstrate that the relative rates of ketone formation (through alternating alkene/CO insertion) and ester formation (through metal acyl alcoholysis) can be tuned across a wide range through judicious bis(phosphine) ligand design. Carbonylative polymerization of 10-undecen-1-ol with a (dppp(3,5-CF 3 ) 4 )Pd(OTs) 2 catalyst (dppp(3,5-CF 3 ) 4 = 1,3-bis[bis[3,5-bis(trifluoromethyl)phenyl]-phosphino]propane) led to the formation of high molecular weight polyketoesters with intermediate dispersity (M n > 20,000 g/mol, D- = 2.6) and a ketone/ester microstructure ratio of approximately 1:2. In these polymerization reactions, deploying electron-deficient bis(phosphines) to suppress deleterious alkene isomerization was the key to accessing the high molecular weight polymer. Further, terpolymerization reactions of 1-hexene/10-undecen-1-ol/CO or 1-fluoro-10-undecene/10-undecen-1-ol/CO by (dppp(3,5-CF 3 ) 4 )Pd(OTs) 2 were also successful. Furthermore, this proof of concept polymerization unlocks access to tunable polymer microstructures without extensive postpolymerization treatment.

10-undecen-1-ol↗
DOE OSTI2025

Mesoporous Amorphous High-Entropy Oxide Films: Unlocking Enhanced Redox Activity

High-entropy oxides (HEOs) represent a frontier in catalyst design via entropy-stabilized solid solution formation. However, their catalytic efficiency is limited by their bulk and dense nature. This work presents a strategic approach to tackle this challenge by fabricating mesoporous amorphous HEO films (MA-HEOF) possessing maximized active site utilization efficiency. The success hinges on the as-developed geometric engineering strategy via controlled deposition–precipitation to confine the amorphous HEO thin film on the surface of mesoporous channels. The unique structure of MA-HEOF was elucidated via microscopy-, X-ray-, and neutron-based techniques, which were manifested by enriched surface-activated lattice oxygen and enhanced redox activity, as confirmed by isotope studies. Besides, the MA-HEOF could stabilize and modulate the properties of integrated noble metal sites, enhancing their redox activity in diverse reactions. In conclusion, the approaches and insights presented herein provide guidance on maximizing the utilization efficiency of high-entropy materials in catalysis and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Alkali Cation-Mediated Modulation of CO 2 Reduction Activity on Tin Electrodes in [EMIM][BF 4 ]/H 2 O Electrolytes

The development of efficient CO 2 reduction technologies hinges upon a thorough understanding of the intricate interplay between solution cations and the characteristics of the electrode surface. Recently, ionic liquids (ILs) have emerged as promising electrolytes for the CO 2 reduction reaction. However, the effect of alkali cations on the electrochemical CO 2 reduction (CO 2 R) reaction remains unclear in ILs. Here, in this report, we studied alkali cation effects by assessing the electrocatalytic CO 2 R activity with the IL 1-ethyl-3-methylimidazolium tetrafluoroborate, [EMIM][BF 4 ], in water with alkali metal co-cations (i.e., Li + , Na + , and K + ) using a polycrystalline Sn catalyst. Contrary to previous findings in purely aqueous media with inorganic cations, where alkali cations strongly enhance CO 2 R via pH modulation and strengthening of interfacial electric fields, alkali cations in electrolytes containing the IL [EMIM][BF4] negatively impact CO 2 R activity on Sn electrodes. These results were attributed to the larger radius and higher concentration of the IL organic cation [EMIM] + that mitigates the impact of alkali cations. These findings highlight the complex interplay between IL cations and alkali metals in shaping CO 2 R performance.

Chemistry↗
DOE OSTI2021

An Active and Robust Air Electrode for Reversible Protonic Ceramic Electrochemical Cells

Reversible protonic ceramic electrochemical cells (RPCECs) are a promising option for efficient and low-cost generation of electricity and hydrogen. Commercialization of R-PCECs, however, hinges on the development of highly active and robust air electrodes. Here, we report an air electrode consisting of PrBa 0.8 Ca 0.2 Co 2 O 5+δ and in situ exsolved BaCoO 3–δ nanoparticles (PBCC–BCO) that shows minimal polarization resistance (~0.24 Ω cm 2 at 600 °C) and high stability when exposed to humidified air with 3–50% H 2 O. An R-PCEC utilizing PBCC-BCO demonstrates remarkable performances at 600 °C: achieving a peak power density of 1.06 W cm –2 in the fuel cell mode and a current density of 1.51 A cm –2 at 1.3 V in an electrolysis mode. More importantly, the RPCECs demonstrate an exceptionally high durability over 1833 h of continuous operation in the electrolysis mode. Furthermore, this work offers an efficient approach to design of high-performance and durable electrodes for R-PCECs.

25 ENERGY STORAGE↗
DOE OSTI2022

Molecular Coatings Improve the Selectivity and Durability of CO 2 Reduction Chalcogenide Photocathodes

The quest for solar-driven conversion of carbon dioxide to chemicals and fuels hinges upon the identification of an efficient, durable, and selective photocathode. Chalcogenide p-type semiconductors exemplified by chalcopyrite Cu(In,Ga)Se 2 (CIGS) have been effectively deployed as photocathodes. However, selectivity toward CO 2 reduction and durability of the commonly used CdS adlayer remain primary challenges. Here, we demonstrate that for the wide band gap CuGa 3 Se 5 chalcopyrite absorber these challenges are well addressed by an organic coating generated in situ from an N,N'-(1,4-phenylene)bispyridinium ditriflate salt in the electrolyte. The molecular additive provides a 30-fold increase in selectivity toward CO2R products compared to the unmodified system and lowers Cd corrosion at least 10-fold. This dual functionality highlights the promise of hybrid solid-state-molecular photocathodes for enabling durable and efficient solar fuel systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Surface Charge in Electrical Double Layer as a Kinetic Descriptor of Electrocatalytic Reactions

The successful commercialization of electrochemical energy-conversion systems hinges on a deeper understanding of electrocatalytic reaction kinetics. Despite extensive research, a key descriptor that characterizes electrolyte effects on reaction kinetics remains elusive. Here, surface charge in electrical double layers (EDLs) is introduced as a descriptor for electrolyte-dependent kinetics. The surface charge is calculated with a continuum EDL model parameterized by density-functional theory. The model is validated by reproducing the anomalously low slope of Pt(111) in Parsons-Zobel plots. Strong correlations are observed between calculated surface charge and experimental kinetic currents for hydrogen evolution, oxygen reduction, and CO 2 -reduction reactions across various pH levels and cationic species. These correlations can be either promotional or inhibitory, depending on solute-intermediate interactions. In acidic media, incorporating adsorbate charge captures specific adsorption effects in oxygen reduction reaction. In conclusion, these findings establish surface charge density as a key descriptor for electrolyte-dependent kinetics, which will guide the design of the electrode/electrolyte interface.

Adsorption↗