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Signs of nonmonotonic finite-volume corrections to 𝑔 𝐴

We study finite-volume (FV) corrections to determinations of 𝑔 𝐴 via lattice quantum chromodynamics (QCD) using analytic results and numerical analysis. We observe that 𝑆⁢𝑈⁡(2) heavy Baryon chiral perturbation theory does not provide an unambiguous prediction for the sign of the FV correction, which is not surprising when one also considers large-𝑁 𝑐 constraints on the axial couplings. We further show that nonmonotonic FV corrections are naturally allowed when one considers either including explicit Δ-resonance degrees of freedom or one works to higher orders in the chiral expansion. We investigate the potential impact of these FV corrections with a precision study of 𝑔 𝐴 using models of FV corrections that are monotonic and nonmonotonic. Using lattice QCD data that is approximately at the 1% level of precision, we do not see significant evidence of nonmonotonic corrections. Looking forward to the next phase of lattice QCD calculations, we estimate that calculations that are between the 0.1% and 1% level of precision may be sensitive to these FV artifacts. Finally, we present an update of the CalLat prediction of 𝑔 𝐴 in the isospin limit with subpercent precision, 𝑔$^{QCD}_{𝐴}$ = 1.2674⁢(96).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

42 ENGINEERING↗

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗

Elemental and Isotopic Yields from T Coronae Borealis: Predictions and Uncertainties

T Coronae Borealis (T CrB) is a symbiotic recurrent nova system expected to undergo its next outburst within the next 2 yr. Recent hydrodynamic simulations have predicted the nucleosynthetic yields for both carbon–oxygen (CO) and oxygen–neon (ONe) white-dwarf models, but without accounting for thermonuclear reaction rate uncertainties. We perform detailed Monte Carlo postprocessing nucleosynthesis calculations based on updated thermonuclear reaction rates and uncertainties from the 2025 evaluation. We quantify the resulting abundance uncertainties and identify the key nuclear reactions that dominate them. Our results show that both the CO and ONe nova models robustly produce characteristic CNO isotopes. More pronounced abundance differences emerge for elements with A ≥ 20. Sulfur is the most robust observational discriminator between the CO and ONe nova models, with a model-to-model difference of a factor of ≈30 and minimal sensitivity to reaction rate uncertainties. Neon, silicon, and phosphorus exhibit even larger abundance differences (factors of ≈150–250), providing strong diagnostic potential. While their predicted yields are subject to larger uncertainties, these remain smaller than the model-to-model differences, allowing these elements to serve as useful, though less precise, tracers of white-dwarf composition. Chlorine, argon, and potassium also differ between models, but the 1σ-abundance ranges for the CO and ONe models overlap, reducing their present usefulness as composition tracers. We find that only nine nuclear reactions dominate the abundance uncertainties of the most diagnostically important isotopes, and their influence is largely independent of the underlying white-dwarf composition. These results provide guidance for future experimental efforts and for interpreting ejecta compositions in the next eruption of T CrB.

Chemical Abundances↗

The 2025 Evaluation of Experimental Thermonuclear Reaction Rates (ETR25)

This work describes the formalism for estimating thermonuclear reaction rates for astrophysical applications, emphasizing modern statistical approaches such as Monte Carlo sampling and Bayesian models. We discuss related topics including the calculation of resonance energies from nuclear Q values, indirect estimates of particle partial widths, and matching of reaction rates at elevated temperatures to statistical model results. We have evaluated available experimental data on cross sections, resonance energies and strengths, partial widths, lifetimes, spin-parities, and spectroscopic factors. Based on these results, we have estimated numerical values of 78 experimental charged-particle thermonuclear reaction rates for target nuclei in the A = 2–40 mass region, for temperatures ranging from 1 MK to 10 GK. For each reaction, three rate values are provided: low, median, and high, corresponding to the 16th, 50th, and 84th percentiles, respectively, of the cumulative reaction rate probability density distribution. Additionally, we present the factor uncertainty of each rate at each temperature grid point. These results enable users to sample the reaction rate probability density in nucleosynthesis calculations, facilitating uncertainty estimates of nuclidic abundances. The rates presented here refer to their laboratory values. For use in stellar model simulations, these values need to be corrected for the effects of thermal excitations of the interacting nuclei. For each reaction, we include graphs that illustrate the fractional contributions to the overall reaction rate along with the associated uncertainty. These visuals are designed to assist both stellar modelers and nuclear experimentalists by identifying the primary sources of rate uncertain=^texttx);ty at specific stellar temperatures. A graphical comparison with earlier Monte Carlo rates is also provided.

Nuclear astrophysics↗

Carbon‐13 Hyperpolarization of α‐Ketocarboxylates with Parahydrogen in Reversible Exchange

Abstract Signal Amplification by Reversible Exchange (SABRE) is a relatively simple and fast hyperpolarization technique that has been used to hyperpolarize the α‐ketocarboxylate pyruvate, a central metabolite and the leading hyperpolarized MRI contrast agent. In this work, we show that SABRE can readily be extended to hyperpolarize 13 C nuclei at natural abundance on many other α‐ketocarboxylates. Hyperpolarization is observed and optimized on pyruvate (P 13C =17 %) and 2‐oxobutyrate (P 13C =25 %) with alkyl chains in the R‐group, oxaloacetate (P 13C =11 %) and alpha‐ketoglutarate (P 13C =13 %) with carboxylate moieties in the R group, and phenylpyruvate (P 13C =2 %) and phenylglyoxylate (P 13C =2 %) with phenyl rings in the R‐group. New catalytically active SABRE binding motifs of the substrates to the hyperpolarization transfer catalyst – particularly for oxaloacetate – are observed. We experimentally explore the connection between temperature and exchange rates for all of these SABRE systems and develop a theoretical kinetic model, which is used to fit the hyperpolarization build‐up and decay during SABRE activity.

McBride, Stephen J. [Department of Chemistry North↗

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

Schuhl, Haley [Donald Danforth Plant Science Cente↗

Photoelectrocatalytic reduction of CO 2 to formate using immobilized molecular manganese catalysts on oxidized porous silicon

The reduction of carbon dioxide (CO 2 ) to formate using molecular catalysts immobilized on high surface area porous silicon is described. Manganese complexes of the type ( R bpy)Mn(CO) 3 Br (bpy = 2,2′-bipyridine) were prepared with silatrane groups on the bpy ligand for attachment to oxide-coated porous silicon (SiO x -porSi). SiO x -porSi wafers were formed by heating hydrogen-terminated p-type porous silicon wafers under air, and the manganese complexes were immobilized on SiO x -porSi by heating at 80°C. The resulting hybrid photoelectrodes are photoelectrocatalysts for CO 2 reduction in acetonitrile containing 2.0 M triethylamine and 2.0 M isopropanol, yielding formate with high selectivity (>96%) and current density (∼0.6 mA/cm 2 ), excellent reproducibility, and a photovoltage of 280 mV at −1.75 V (versus ferrocenium/ferrocene) under 1 sun illumination. Here, the applied potential is close to the equilibrium potential for CO 2 reduction to formate. This work presents rare examples of immobilized molecular catalysts for CO 2 reduction to formate and the first on semiconducting silicon.

13 HYDRO ENERGY↗

Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases

Background Large language models (LLMs) are increasingly used medicine for diverse applications including differential diagnostic support. The training data used to create LLMs such as the Generative Pretrained Transformer (GPT) predominantly consist of English-language texts, but LLMs could be used across the globe to support diagnostics if language barriers could be overcome. Initial pilot studies on the utility of LLMs for differential diagnosis in languages other than English have shown promise, but a large-scale assessment on the relative performance of these models in a variety of European and non-European languages on a comprehensive corpus of challenging rare-disease cases is lacking. Methods We created 4917 clinical vignettes using structured data captured with Human Phenotype Ontology (HPO) terms with the Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema. These clinical vignettes span a total of 360 distinct genetic diseases with 2525 associated phenotypic features. We used translations of the Human Phenotype Ontology together with language-specific templates to generate prompts in English, Chinese, Czech, Dutch, French, German, Italian, Japanese, Spanish, and Turkish. We applied GPT-4o, version gpt-4o-2024-08-06, and the medically fine-tuned Meditron3-70B to the task of delivering a ranked differential diagnosis using a zero-shot prompt. An ontology-based approach with the Mondo disease ontology was used to map synonyms and to map disease subtypes to clinical diagnoses in order to automate evaluation of LLM responses. Findings For English, GPT-4o placed the correct diagnosis at the first rank 19.9% and within the top-3 ranks 27.0% of the time. In comparison, for the nine non-English languages tested here the correct diagnosis was placed at rank 1 between 16.9% and 20.6%, within top-3 between 25.4% and 28.6% of cases. The Meditron3 model placed the correct diagnosis within the first 3 ranks for 20.9% of cases in English and between 19.9% and 24.0% for the other nine languages. Interpretation The differential diagnostic performance of LLMs across a comprehensive corpus of rare-disease cases was largely consistent across the ten languages tested. This suggests that the utility of LLMs in clinical settings may extend to non-English clinical settings.

Artificial intelligence↗

Generalized fiducial inference on differentiable manifolds

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.

97 MATHEMATICS AND COMPUTING↗

Understanding X-ray bursts via direct measurements of ( α , p ) reactions

X-ray bursts, which are thermonuclear explosions on neutron stars accreting mass from companion stars, are the most frequent stellar explosions. To understand the mechanisms behind these bursts, astrophysical models need to incorporate the reaction rates of key nuclear reactions occurring in these stellar environments. This, however, presents significant challenges due to the typically small cross sections of these reactions and the experimental complexities associated with the use of low-intensity radioactive beams. Consequently, most of these reaction rates remain unknown. However, recent advancements in radioactive ion beam facilities and experimental techniques have opened new opportunities to measure these crucial astrophysical reactions. Furthermore, this work explores these advances and highlights recent measurements relevant to X-ray bursts.

Avila, M. L. [Argonne National Laboratory (ANL), A↗

Deciphering Reaction Mechanisms of Molecular Proton Reduction Catalysts with Cyclic Voltammetry: Kinetic vs Thermodynamic Control

The kinetics and thermodynamics of elementary reaction steps involved in the catalytic reduction of protons to hydrogen define the reaction landscape for catalysis. The mechanisms can differ in the order of the elementary proton transfer, electron transfer, and bond-forming steps and can be further differentiated by the sites at which protons and electrons localize. Access to fully elucidated mechanistic, kinetic, and thermochemical details of molecular catalysts is crucial to facilitate the development of new catalysts that operate with optimal efficiency, selectivity, and durability. The mechanism by which a catalyst operates, as well as the kinetics and thermodynamics associated with the individual steps, can often be accessed through electroanalytical studies. Here, this Account details the application of cyclic voltammetry to interrogate reaction mechanisms and quantify the kinetics and thermodynamics of elementary reaction steps for a series of molecular catalysts that mediate electrochemical proton reduction. I distinguish the limiting scenarios wherein a catalyst operates under kinetic control vs thermodynamic control, with a focus on detecting how cyclic voltammetry features shift with proton source strength and concentration, as well as scan rate. For systems that operate under kinetic control, catalytic currents are observed at, or slightly positive toward, the formal potential for the redox process that triggers catalysis. Under thermodynamic control, catalytic responses shift as a function of the proton source pKa and effective pH of the solution. After drawing this distinction, we introduce the appropriate voltammetry experiments and accompanying analytical expressions for extracting key metrics from the data. To illustrate analytical strategies to quantify elementary reaction steps of catalysts operating under kinetic control, I describe our studies of proton reduction catalysts Co(dmgBF 2 ) 2 (CH 3 CN) 2 (dmgBF 2 = difluoroboryl-dimethylglyoxime) and [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ (P 2 Ph N 2 Ph = 1,5-phenyl-3,7-phenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, peak shift analysis, foot-of-the-wave analysis, and plateau current analysis are applied to data sets wherein voltammetric response are recorded as a function of catalyst concentration, proton source concentration, proton source strength, and scan rate to quantify rate constants for elementary proton transfer and bond-forming steps in a catalytic cycle. Further, the case study of [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ illustrates how complementary spectroscopic methods can bolster the mechanistic assignment. Collectively, these two studies showcase how detailed mechanistic studies inform on rate-limiting elementary steps in catalysis and other key processes underpinning catalysis. Second, I present analytical strategies to interrogate catalysts operating under thermodynamic control, centered on the case study of [Ni II (P 2 Ph N 2 Bn ) 2 ] 2+ (P 2 Ph N 2 Bn = 1,5-dibenzyl-3,7-diphenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, the application of nonaqueous Pourbaix theory to extract thermodynamic information is introduced, and the construction of a coupled Pourbaix diagram is detailed. This study identifies ligand-based protonation as the key process that places catalysis under thermodynamic control and influences the reaction mechanism. Together, the work detailed in this Account showcases the utility of electroanalytical methods to disentangle complex reaction mechanisms and extract key thermochemical and kinetic parameters for elementary steps of catalysis. Through detailed presentation of the key analytical expressions that underpin these analyses, this Account seeks to facilitate the adoption of cyclic voltammetry by the community to fully extract kinetic, thermochemical, and mechanistic information on electrochemical small-molecule activation.

catalysts↗

Molecular Modifications of Crystalline Poly(triazine imide) for Advancing Its Structure–Property Relationships in Light-Driven Catalysis

Carbon-nitride materials represent light-absorbing structures composed of earth-abundant elements capable of being leveraged for semiconductor photocatalysis at their surfaces. This study systematically investigates the addition of molecular modifiers to the synthesis of crystalline carbon nitrides to assess their effects on the materials’ structure, optical bandgap, and photocatalytic activity for hydrogen (H 2 ) and oxygen (O 2 ) evolution under ultraviolet and visible-light irradiation. Melamine and five pyrimidine-centered analogs were employed as building blocks to modify various heteroatoms within the polymeric framework. The modified materials were characterized with attention to the differences introduced by the monomeric modifiers and their influence on the resulting structures and compositions. The findings indicate that these changes significantly broaden the visible-light absorption range, albeit with the gradual loss of the bulk crystalline structure. As the loading of modifiers increased beyond 50%, a predominantly amorphous form of carbon nitride emerged. XPS, 13 C solid-state NMR, and SEM analyses corroborated the changes, which were attributed to modifications of the elemental composition and a reduced amount of Li cations and charge-balancing Cl anions owing to fewer binding sites in the intralayer cavities. In photocatalytic measurements under an ultraviolet 390 nm LED, and aided by photodeposited nanoparticle cocatalysts, the unmodified PTI-LiCl framework demonstrated the highest H 2 evolution rate (HER; 3.44 mmol·g –1 ·h –1 ) with an apparent quantum yield of 5.4%, along with total water splitting at rates of 163 μmol of H 2 ·g –1 ·h –1 and 75.6 μmol·O 2 g –1 ·h –1 . While PTI-LiCl showed trace activity under a visible-light 440 nm LED, all modified materials exhibited enhanced reactivity with as low as 5% molecular modifiers. The photocatalytic rates peaked at a 15% modification level when using 2,4,6-triaminopyrimidine, with rates of 33 μmol·g –1 ·h –1 for HER, along with 19.7 μmol of H 2 ·g –1 ·h –1 and 8.7 μmol of O 2 ·g –1 ·h –1 for total water splitting. Density functional theory calculations were used to probe electronic structure changes resulting from the modifications. Furthermore, these results elucidate the structural, optical, and electronic changes arising from the five selected molecular modifiers and their impact on the semiconductors’ photocatalytic properties.

Electrical conductivity↗

Advancing Sodium-Ion Battery Cathodes: A Low-Cost, Eco-Friendly Mechanofusion Route from TiO 2 Coating to Ti 4+ Doping

Layered oxide battery cathodes often require extra stabilization strategies, such as surface coating or doping, to mitigate side reactions and enhance longevity. Conventional methods such as aqueous deposition and atomic layer deposition are costly and environmentally unfriendly and even damage the original structure, especially for air-sensitive sodium-ion battery (SIB) cathodes. Herein, we introduce an all-dry mechanofusion technique that modifies hydroxide precursors with TiO 2 coating before sintering with a sodium source. Using advanced characterizations including X-ray diffraction, neutron diffraction, and solid-state nuclear magnetic resonance for structural insights, X-ray absorption spectroscopy to study metal valence states, and transmission X-ray microscopy for nanoscale visualization of nickel oxidation states, we verified that postsintering transforms TiO 2 surface coating into Ti doping, leading to improved Ni-oxidation homogeneity, modified charge compensation, and enhanced thermal stability. Electrochemical tests reveal superior performance in capacity retention, rate capability, and air stability for these modified cathodes, with pouch cells maintaining over 85% capacity after 650 cycles. This method presents a sustainable, cost-effective route for advanced SIB cathode development.

36 MATERIALS SCIENCE↗

Following CO and H Insertion into Ru–C Bonds with X-ray Photoelectron and Absorption Spectroscopies

Insertion reactions play a central role in the catalytic synthesis of ethanol and higher alcohols. X-ray photoelectron and absorption spectroscopies have been used to follow migratory CO insertion and C─C coupling in a cis-[Ru(2,2′-bipyridine) 2 (CO)(CH 3 )] + complex heated in a vacuum or exposed to CO. Heating of the Ru complex in a vacuum to temperatures above 50 °C induced spontaneous migration of CO into the Ru─CH 3 bond to yield a ─COCH 3 ligand. In conclusion, after adding CO to the background gas, the CO insertion reaction was seen at room temperature, opening the door for the synthesis of ethanol and more energy dense liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exfoliation of Cu-Containing Poly(triazine imide): From Three-Dimensional to Two-Dimensional Particle Morphology

Controlling the morphological parameters of extended covalent organic frameworks remains challenging and represents an important yet often elusive metric of consideration. Typically, carbon nitride materials possess local ordering but remain largely amorphous in terms of their long-range order and orientation. This study probes the synthesis of a crystalline carbon nitride, poly(triazine imide) lithium bromide which possesses an atomically-precise extended structure, and demonstrates its exfoliation into a two-dimensional hexagonal sheet-like morphology. Furthermore, a previously unreported carbon nitride material, poly(triazine imide) copper bromide, or PTI-CuBr, was developed through an additional flux-assisted cation-exchange process and is shown to retain its internal Cu cations during solvothermal exfoliation. Characterization by dynamic light scattering and high-angle annular dark-field scanning electron microscopy reveals the morphological changes and captures the high aspect ratio of the thin carbon nitride sheets with <10 nm thickness while maintaining hundreds of nm in width. Additional characterization by energy-dispersive spectroscopy and X-ray photoelectron spectroscopy confirms that the Cu:Br:N molar ratio was maintained within the extended layers throughout the exfoliation process. This top-down synthesis approach differs from typical methods that isolate thin sheets for subsequent metal−cation coordination and illustrates the importance of maintaining oxygen-free conditions to minimize copper clustering. Thus, this new approach is demonstrated to provide a consistent and more homogeneous occupancy of the PTI pore spaces throughout the carbon nitride framework.

Exfoliation↗

Unusual Electrochemical Activity of Thin SiO 2 Layers Leads to Instability of Molecular Attachment in Hybrid Photoelectrodes

Hybrid photoelectrodes, comprised of a light-absorbing semiconductor and a surface-integrated molecular catalyst, are attractive for applications in artificial photosynthesis, since they combine the advantages of broadband semiconductor light absorption with the selectivity of molecular catalysis. A widely used class of hybrid photoelectrodes is based on Si substrates passivated by a thin (<3 nm) layer of silicon oxide, which is commonly prepared by controlled chemical or thermal oxidation, resulting in chemical oxide (ChO) or thermal oxide (ThO) layers, respectively. However, the electrochemical stability of these oxide layers, and the chemical stability of the semiconductor-molecule assembly in hybrid photoelectrodes, are not well understood, with evidence that covalently-bound molecules detach from the oxide surface upon application of cathodic bias. We have examined the intrinsic electrochemical reactivity of silicon oxide layers and how it affects the attachment of molecular monolayers. We determined that the surface of Si|ThO is primarily terminated with hydrophobic siloxane moieties, whereas that of Si|ChO contains a higher concentration of hydrophilic silanol groups. Initial high current densities for Si|ChO under applied bias up to -2 V vs. Ag/AgCl, decrease during repeated cyclic voltammetry scans, due to the consumption of surface-bound water. This is manifested by a reversible wave around -0.5 V in CH 3 CN solution, and a similar pH-dependent wave in water, revealing the pK a of the silanol groups to be ~4. Here, our combined observations support the electrochemically-induced dehydration of the SiO 2 surface, which converts silanol groups to siloxanes and proceeds through an H-atom intermediate that is most likely stabilized by pentavalent Si. We propose that similar reactivity is responsible for the electrochemical loss of alkylsiloxane-attached molecules under cathodic bias, which has important implications for the choice of catalyst attachment strategy in hybrid photoelectrodes.

14 SOLAR ENERGY↗