Bias-free solar hydrogen production at 19.8???mA???cm???2 using perovskite photocathode and lignocellulosic biomass

Solar hydrogen production is one of the ultimate technologies needed to realize a carbon-neutral, sustainable society. However, an energy-intensive water oxidation half-reaction together with the poor performance of conventional inorganic photocatalysts have been big hurdles for practical solar hydrogen production. Here we present a photoelectrochemical cell with a record high photocurrent density of 19.8???mA???cm???2 for hydrogen production by utilizing a high-performance organic???inorganic halide perovskite as a panchromatic absorber and lignocellulosic biomass as an alternative source of electrons working at lower potentials. In addition, value-added chemicals such as vanillin and acetovanillone are produced via the selective depolymerization of lignin in lignocellulosic biomass while cellulose remains close to intact for further utilization. This study paves the way to improve solar hydrogen productivity and simultaneously realize the effective use of lignocellulosic biomass

Molecular design of heterogeneous electrocatalysts using tannic acid-derived metal-phenolic networks

Electrochemistry can play a critical role in the transition to a more sustainable society by enabling the carbon-neutral production and use of various chemicals as well as efficient use of renewable energy resources. The prerequisite for the practical application of various electrochemical energy conversion and storage technologies is the development of efficient and robust electrocatalysts. Recently, molecularly designed heterogeneous catalysts have drawn great attention because they combine the advantages of both heterogeneous solid and homogeneous molecular catalysts. In particular, recently emerged metal phenolic networks (MPNs) show promise as electrocatalysts for various electrochemical reactions due to their unique features. They can be easily synthesized under mild conditions, making them eco-friendly, form uniform and conformal thin films on various kinds of substrates, accommodate various metal ions in a single-atom manner, and have excellent charge-transfer ability. In this minireview, we summarize the development of various MPN-based electrocatalysts for diverse electrochemical reactions, such as the hydrogen evolution reaction, the oxygen evolution reaction, the CO2 reduction reaction, and the N2 reduction reaction. We believe that this article provides insight into molecularly designable heterogeneous electrocatalysts based on MPNs and guidelines for broadening the applications of MPNs as electrocatalysts

Demonstration of a roll-to-roll-configurable, all-solution-based progressive assembly of flexible transducer devices consisting of functional nanowires on micropatterned electrodes

Abstract We demonstrate continuous fabrication of flexible transducer devices consisting of interdigitated (IDT) Ag microelectrodes interconnected by ZnO nanowires (ZNWs), created via serially connected solution-processable micro- and nanofabrication processes. On an Ag layer obtainable from the mild thermal reduction of an ionic Ag ink coating, the roll-to-roll-driven photolithography process [termed photo roll lithography (PRL)] followed by wet-etching can be applied to continuously define the IDT microelectrode structure. Conformal ZNWs can then be grown selectively on the Ag electrodes to interconnect them via an Ag-mediated hydrothermal ZNW growth that does not require high-temperature seed sintering. Given that all of these constitutive processes are vacuum-free and solution-processable at a low temperature, and are compatible with continuous processing onto flexible substrates, they can be eventually configured into the roll-to-roll-processable progressive assembly. Through parametric optimizations of processes consisting of the roll-to-roll-configurable, solution-based progressive assembly of nanostructures (ROLSPAN), a flexible transducer consisting of ZNW-interconnected, PRL-ed IDT Ag electrodes can be developed. This flexible architecture faithfully performs UV sensing as well as optoelectronic transduction. The ROLSPAN concept along with its specific applicability to flexible devices may inspire many diverse functional systems requiring high-throughput low-temperature fabrication over large-area flexible substrates

Efficient and Stable Tin???Lead Perovskite Photoconversion Devices Using Dual???Functional Cathode Interlayer

Tin???lead halide perovskites (TLHPs) are promising photoactive materials for photovoltaics (PVs) due to reduced toxicity and broad light absorption. However, their inherent ionic vacancies facilitate inward metal diffusion, accelerating device degradation. Here, efficient, stable TLHP-based PV and photoelectrochemical (PEC) devices are reported containing a chemically protective cathode interlayer???amine-functionalized perylene diimide (PDINN). Solution-processed PDINN effectively extract electrons and suppress inward-metal diffusion by forming tridentate metal complexes with its nucleophilic sites. The PV device achieved an efficiency of 23.21% (>81% retention after 750 h at 60 ??C and >90% retention after 3100 h at 23 ?? 4 ??C), and the first demonstration of TLHP-based PEC devices exhibit a record-high bias-free solar hydrogen production rate (33.0 mA cm???2; ???3.42 ?? 10???6 kg s???1 m???2) when coupled with biomass oxidation, which is ???1.7-fold higher than the ultimate target set by the U.S. Department of Energy for one-sun hydrogen production. These findings demonstrate the potential of TLHPs for efficient, stable photoconversion by the molecular design of the cathode interlayer

Robust Machine Learning Inference from X‑ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. In this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community