Interfacial Strain-Modulated Nanospherical Ni2 P by Heteronuclei-Mediated Growth on Ti3 C2 Tx MXene for Efficient Hydrogen Evolution.

Interface modulation of nickel phosphide (Ni2 P) to produce an optimal catalytic activation barrier has been considered a promising approach to enhance the hydrogen production activity via water splitting. Herein, heteronuclei-mediated in situ growth of hollow Ni2 P nanospheres on a surface defect-engineered titanium carbide (Ti3 C2 Tx ) MXene showing high electrochemical activity for the hydrogen evolution reaction (HER) is demonstrated. The heteronucleation drives intrinsic strain in hexagonal Ni2 P with an observable distortion at the Ni2 P@Ti3 C2 Tx MXene heterointerface, which leads to charge redistribution and improved charge transfer at the interface between the two components. The strain at the Ni2 P@Ti3 C2 Tx MXene heterointerface significantly boosts the electrochemical catalytic activities and stability toward HER in an acidic medium via a combination between experimental results and theoretical calculations. In a 0.5 m H2 SO4 electrolyte, the Ni2 P@Ti3 C2 Tx MXene hybrid shows excellent HER catalytic performance, requiring an overpotential of 123.6 mV to achieve 10 mA cm-2 with a Tafel slope of 39 mV dec-1 and impressive durability over 24 h operation. This approach presents a significant potential to rationally design advanced catalysts coupled with 2D materials and transition metal-based compounds for state-of-the-art high efficiency energy conversions.

Evidence-based recommender system for high-entropy alloys.

Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster-Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe-Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

Core-Shell Structured MXene@Carbon Nanodots as Bifunctional Catalysts for Solar-Assisted Water Splitting.

The design of nonprecious bifunctional electrocatalysts with high activity and prolonged durability in a wide pH range is essential for the development of the highly efficient, cost-effective, and simplified overall water splitting systems. Here, we report core-shell structured MXene@carbon (MX@C) nanodot hybrids with high bifunctional activity, where N-doped carbon shells are grown in a heteroepitaxial manner strongly interacting with the MXene core. The resulting MX@C nanodot hybrids show enhanced catalytic activity for electrochemical hydrogen evolution reaction (HER) in various pH media from 0 to 14. At pH 14, MX@C achieves the low onset potential of 134 mV at 10 mA/cm2 and reduced Tafel slope of 32 mV/dec due to the facilitated charge transfer along the recombination reaction. For the oxygen evolution reaction (OER), MX@C nanodots are incorporated onto the surface of molybdenum-doped bismuth vanadate (Mo:BiVO4) as a cocatalyst of the photoanode, thereby achieving 1.5 times higher photocurrent density than pristine Mo:BiVO4 at 1.23 V (vs reversible hydrogen electrode) due to the enhanced light absorption and charge transfer efficiency. The superiority of this hybrid catalyst is demonstrated implementing the solar-assisted overall water splitting cells based on the MX@C cathode and MX@C/Mo:BiVO4 photoanode. These cells show the enhancement of current density from 0.78 to 1.23 mA/cm2 with long-term durability over 8 h. These results are attributed to the facile surface catalytic kinetics of the chemically and electronically coupled MX@C hybrid at the heterointerface for both OER and HER.

Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship.

New Nd-Fe-B crystal structures can be formed via the elemental substitution of LA-T-X host structures, including lanthanides (LA), transition metals (T) and light elements, X = B, C, N and O. The 5967 samples of ternary LA-T-X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the LA-T-X host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures.

Boron cage effects on Nd-Fe-B crystal structure's stability.

In this study, we investigate the structure-stability relationship of hypothetical Nd-Fe-B crystal structures using descriptor-relevance analysis and the t-SNE dimensionality reduction method. 149 hypothetical Nd-Fe-B crystal structures are generated from 5967 LA-T-X host structures in the Open Quantum Materials Database by using the elemental substitution method, with LA denoting lanthanides, T denoting transition metals, and X denoting light elements such as B, C, N, and O. By borrowing the skeletal structure of each of the host materials, a hypothetical crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High-throughput first-principle calculations are applied to evaluate the phase stability of these structures. Twenty of them are found to be potentially formable. As the first investigative result, the descriptor-relevance analysis on the orbital field matrix (OFM) materials' descriptor reveals the average atomic coordination number as the essential factor in determining the structure stability of these substituted Nd-Fe-B crystal structures. 19 among 20 hypothetical structures that are found potentially formable have an average coordination number larger than 6.5. By applying the t-SNE dimensionality reduction method, all the local structures represented by the OFM descriptors are integrated into a visible space to study the detailed correlation between their characteristics and the stability of the crystal structure to which they belong. We discover that unstable substituted structures frequently carry Nd and Fe local structures with two prominent points: low average coordination numbers and fully occupied B neighboring atoms. Moreover, there are only three popular forms of B local structures appearing on all potentially formable substituted structures: cage networks, planar networks, and interstitial sites. The discovered relationships are promising to speed up the screening process for the new formable crystal structures.

Biopolymer-Inspired N-Doped Nanocarbon Using Carbonized Polydopamine: A High-Performance Electrocatalyst for Hydrogen-Evolution Reaction.

Hydrogen-evolution reaction (HER) is a promising technology for renewable energy conversion and storage. Electrochemical HER can provide a cost-effective method for the clean production of hydrogen. In this study, a biomimetic eco-friendly approach to fabricate nitrogen-doped carbon nanosheets, exhibiting a high HER performance, and using a carbonized polydopamine (C-PDA), is described. As a biopolymer, polydopamine (PDA) exhibits high biocompatibility and can be easily obtained by an environmentally benign green synthesis with dopamine. Inspired by the polymerization of dopamine, we have devised the facile synthesis of nitrogen-doped nanocarbons using a carbonized polydopamine for the HER in acidic media. The N-doped nanocarbons exhibit excellent performance for H2 generation. The required overpotential at 5 mA/cm2 is 130 mV, and the Tafel slope is 45 mV/decade. Experimental characterizations confirm that the excellent performance of the N-doped nanocarbons can be attributed to the multisite nitrogen doping, while theoretical computations indicate the promotion effect of tertiary/aromatic nitrogen doping in enhancing the spin density of the doped samples and consequently in forming highly electroactive sites for HER applications.

Committee machine that votes for similarity between materials.

A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

Graphene-Embedded Hydrogel Nanofibers for Detection and Removal of Aqueous-Phase Dyes.

A facile route to graphene/polymer hydrogel nanofibers was developed. An aqueous dispersion of graphene (containing >40% bilayer graphene flakes) stabilized by a functionalized water-soluble polymer with phenyl side chains was successfully electrospun to yield nanofibers. Subsequent vapor-phase cross-linking of the nanofibers produced graphene-embedded hydrogel nanofibers (GHNFs). Interestingly, the GHNFs showed chemical sensitivity to the cationic dyes methylene blue (MB) and crystal violet (CV) in the aqueous phase. The adsorption capacities were as high as 0.43 and 0.33 mmol g-1 s-1 for MB and CV, respectively, even in a 1.5 mL s-1 flow system. A density functional theory calculation revealed that aqueous-phase MB and CV dyes were oriented parallel to the graphene surface and that the graphene/dye ensembles were stabilized by secondary physical bonding mechanisms such as the π-π stacking interaction in an aqueous medium. The GHNFs exhibited electrochemical properties arising mainly from the electric double-layer capacitance, which were applied in a demonstration of GHNF-based membrane electrodes (5 cm in diameter) for detecting the dyes in the flow system. It is believed that the GHNF membrane can be a successful model candidate for commercialization of graphene due to its easy-to-fabricate process and remarkable properties.

A near-infrared "turn-on" fluorescent probe with a self-immolative linker for the in vivo quantitative detection and imaging of hydrogen sulfide.

Hydrogen sulfide is a critical biological messenger, but few biologically compatible methods are available for its detection in vivo. Here, we describe the design and synthesis of a novel azide-functionalized near-infrared probe, NIR-Az, for a hydrogen sulfide assay in which a self-immolative linker is incorporated between the azide moiety and phenolic dihydroxanthene fluorophore from a cyanine dye. A large "turn-on" near-infrared fluorescence signal results from the reduction of the azide group of the fluorogenic moiety to an amine, in which the self-immolative linker also enhances the accessibility of NIR-Az to hydrogen sulfide. NIR-Az can select hydrogen sulfide from among 16 analytes, including cysteine, glutathione, and homocysteine. By exploiting the superior properties of NIR-Az, such as its good biocompatibility and rapid cell internalization, we successfully demonstrated its usefulness in monitoring both the concentration- and time-dependent variations of hydrogen sulfide in living cells and animals (detection limit less than 0.26µM), thereby providing a powerful approach for probing hydrogen sulfide chemistry in biological systems.

Recent Advances in Nanostructured Conducting Polymers: from Synthesis to Practical Applications.

Conducting polymers (CPs) have been widely studied to realize advanced technologies in various areas such as chemical and biosensors, catalysts, photovoltaic cells, batteries, supercapacitors, and others. In particular, hybridization of CPs with inorganic species has allowed the production of promising functional materials with improved performance in various applications. Consequently, many important studies on CPs have been carried out over the last decade, and numerous researchers remain attracted to CPs from a technological perspective. In this review, we provide a theoretical classification of fabrication techniques and a brief summary of the most recent developments in synthesis methods. We evaluate the efficacy and benefits of these methods for the preparation of pure CP nanomaterials and nanohybrids, presenting the newest trends from around the world with 205 references, most of which are from the last three years. Furthermore, we also evaluate the effects of various factors on the structures and properties of CP nanomaterials, citing a large variety of publications.