Search

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. We discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

Amine-functionalized porous solid materials are effective sorbents for direct air capture (DAC) of CO2. However, they are prone to oxidative degradation in service, increasing the materials cost for widespread implementation. While the identification of oxidation products has given insights into degradation pathways, the roles of some species, like CO2itself, remain unresolved, with conflicting information in the literature. Here, we investigate the impact of CO2on the oxidative degradation of poly(ethylenimine)–alumina (PEI/Al2O3) sorbents under conditions encompassing a wide range of CO2-air mixture compositions and temperatures relevant to DAC conditions, thereby reconciling the conflicting data in the literature. Degradation profiles characterized by thermogravimetric analysis, in situATR-FTIR, and CO2capacity measurements reveal nonmonotonic effects of CO2concentrations and temperatures on oxidation kinetics. Specifically, 0.04% CO2accelerates PEI/Al2O3oxidation more at low temperatures (<90 °C) compared to 1% and 5% CO2, but this trend reverses at high temperatures (>90 °C). First-principles metadynamics, machine learning accelerated molecular dynamics simulations, and 1H relaxometry experiments show that chemisorbed CO2acid-catalyzes critical oxidation reactions, while extensive CO2uptake reduces PEI branch mobility, slowing radical propagation. These contrasting kinetic effects of CO2explain the complex degradation profiles observed in this work and in prior literature. Collectively, this work highlights the importance of considering atmospheric components in the design of DAC sorbents and processes. Additionally, it identifies the unconstrained branch mobility and local acid environment as two of the major culprits in the oxidation of amine-based sorbents, suggesting potential strategies to mitigate sorbent degradation.

Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO2slit-pores. We find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO2interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen–oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.

High-throughput electronic structure calculations (often performed using density functional theory (DFT)) play a central role in screening existing and novel materials, sampling potential energy surfaces, and generating data for machine learning applications. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal DFT and furnish a more accurate description of the underlying electronic structure, albeit at a computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed a robust, accurate, and computationally efficient framework for high-throughput condensed-phase hybrid DFT and implemented this approach in the PWSCFmodule of Quantum ESPRESSO(QE). The resulting SeAapproach (SeA= SCDM + exx+ ACE) combines and seamlessly integrates: (i) the selected columns of the density matrix method (SCDM, a robust noniterative orbital localization scheme that sidesteps system-dependent optimization protocols), (ii) a recently extended version of exx(a black-box linear-scaling EXX algorithm that exploits sparsity between localized orbitals in real space when evaluating the action of the standard/full-rank V^xxoperator), and (iii) adaptively compressed exchange (ACE, a low-rank V^xxapproximation). In doing so, SeAharnesses three levels of computational savings: pair selectionand domain truncationfrom SCDM + exx(which only considers spatially overlapping orbitals on orbital-pair-specific and system-size-independent domains) and low-rankV^xxapproximationfrom ACE (which reduces the number of calls to SCDM + exxduring the self-consistent field (SCF) procedure). Across a diverse set of 200 nonequilibrium (H2O)64configurations (with densities spanning 0.4–1.7 g/cm3), SeAprovides a 1−2 order-of-magnitude speedup in the overall time-to-solution, i.e., ≈8−26× compared to the convolution-based PWSCF(ACE)implementation in QEand ≈78−247× compared to the conventional PWSCF(Full)approach, and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeAvia an actively learned data set with ≈8,700 (H2O)64configurations. Using an out-of-sample set of (H2O)512configurations (at nonambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeAby computing the ground-truth ionic forces in this challenging system containing >1,500 atoms.

Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO2films. It is shown that even for SiO2pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.

As a promising layered electrode material, TiS2-based capacitive deionization (CDI) devices for water desalination have attracted significant attention. However, TiS2/H2O interfacial features, potentially important for device optimization, remain unidentified. Using Deep Potential Molecular Dynamics (DPMD), we characterized distinct aqueous interfaces introduced by four TiS2terminations expected to be present as water intercalates into TiS2, namely, Armchair, Zigzag, Zigzag-L, and Zigzag-R. First, we assessed important representative physical properties of the system to validate the deep potentials (DPs). DPMD simulations agree well with experiments and first-principles simulations, suggesting the DPs are accurate and reliable. Subsequent simulations of these TiS2/water interfaces revealed how TiS2surface termination influences the structure of interfacial water. This effect is most evident in the first and second water layers close to the TiS2surface, and more pronounced when spontaneous dissociative adsorption of water occurs. The extent of water dissociation on each surface was evaluated using enhanced sampling. Zigzag-L is the only interface where proton transfer from adsorbed water to TiS2surface S atoms is thermodynamically and kinetically favored. The coexistence of surface four-fold-coordinated Ti (Ti4c) and one-fold-coordinated S (S1c) is found to be essential to making proton transfer feasible on the Zigzag-L surface. Furthermore, remaining unprotonated S1catoms can act as good proton acceptors after water dissociation. Thus, TiS2with Zigzag-L termination may be a surface to avoid in CDI device construction, given that pH fluctuations adversely affect performance. This work provides new understanding of TiS2/H2O interfacial features that could aid future design and optimization of TiS2-based CDI devices for water desalination.

Presents letters to the editor referencing articles and topics discussed in previous issues. "New Frontiers for Fun," which discussed high technology adventure; "The Japanese Recovery," which discussed the economic recovery in Japan; "Of Prostitutes and Their Patrons," which discussed prostitution in China; Other letters to the editor.