Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions.

Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.

Effect of cross-linking density on the rheological behavior of ultra-soft chitosan microgels at the oil-water interface.

In this paper, microgels with uniform particle size were prepared by physically cross-linking the hydrophobically modified chitosan (h-CS) with sodium phytate (SP). The effects of cross-linking density on the interfacial adsorption kinetics, viscoelasticity, stress relaxation, and micorheological properties of the hydrophobically modified chitosan microgels (h-CSMs) at the oil-water interface were extensively investigated by the dilatational rheology, compressional rheology, and particle tracing microrheology. The results were correlated with the particle size, morphology, and elasticity of the microgels characterized by dynamic light scattering and atomic force microscopy.

Accessing complex reconstructed material structures with hybrid global optimization accelerated on-the-fly machine learning.

The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures.

Characterization of a Dihydromyricetin/α-Lactoalbumin Covalent Complex and Its Application in Nano-emulsions.

A dihydromyricetin (DMY)/α-lactoalbumin (α-La) covalent complex was prepared and characterized, and its application in nano-emulsions was also evaluated in this study. The results suggested that the covalent complex could be obtained using the alkaline method. The UV and IR spectra confirmed the formation of the covalent complex, and the amount of DMY added was positively correlated with the total phenol content of the complex.

Guiding catalytic CO reduction to ethanol with copper grain boundaries.

The grain boundaries (GBs) in copper (Cu) electrocatalysts have been suggested as active sites for CO electroreduction to ethanol. Nevertheless, the mechanisms are still elusive. Herein, we describe how GBs tune the activity and selectivity for ethanol on two representative Cu-GB models, namely Cu∑3/(111) GB and Cu∑5/(100) GB, using joint first-principles calculations and experiments.

Modulation of *CHO Adsorption to Facilitate Electrocatalytic Reduction of CO to CH over Cu-Based Catalysts.

Copper (Cu) can efficiently catalyze the electrochemical CO reduction reaction (CORR) to produce value-added fuels and chemicals, among which methane (CH) has drawn attention due to its high mass energy density. However, the linear scaling relationship between the adsorption energies of *CO and *CHO on Cu restricts the selectivity toward CH. Alloying a secondary metal in Cu provides a new freedom to break the linear scaling relationship, thus regulating the product distribution.

High-Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor.

Developing easily accessible descriptors is crucial but challenging to rationally design single-atom catalysts (SACs). This paper describes a simple and interpretable activity descriptor, which is easily obtained from the atomic databases. The defined descriptor proves to accelerate high-throughput screening of more than 700 graphene-based SACs without computations, universal for 3-5d transition metals and C/N/P/B/O-based coordination environments.

Selective CO electroreduction to methanol via enhanced oxygen bonding.

The reduction of carbon dioxide using electrochemical cells is an appealing technology to store renewable electricity in a chemical form. The preferential adsorption of oxygen over carbon atoms of intermediates could improve the methanol selectivity due to the retention of C-O bond. However, the adsorbent-surface interaction is mainly related to the d states of transition metals in catalysts, thus it is difficult to promote the formation of oxygen-bound intermediates without affecting the carbon affinity.

Support stabilized PtCu single-atom alloys for propane dehydrogenation.

PtCu single-atom alloys (SAAs) open an extensive prospect for heterogeneous catalysis. However, as the host of SAAs, Cu suffers from severe sintering at elevated temperature, resulting in poor stability of catalysts. This paper describes the suppression of the agglomeration of Cu nanoparticles under high temperature conditions using copper phyllosilicate (CuSiO) as the support of PtCu SAAs.

Data-Driven Interpretable Descriptors for the Structure-Activity Relationship of Surface Lattice Oxygen on Doped Vanadium Oxides.

Understanding the structure-activity relationship of surface lattice oxygen is critical but challenging to design efficient redox catalysts. This paper describes data-driven redox activity descriptors on doped vanadium oxides combining density functional theory and interpretable machine learning. We corroborate that the p-band center is the most crucial feature for the activity.

Nature of the Active Sites of Copper Zinc Catalysts for Carbon Dioxide Electroreduction.

The electrochemical CO reduction (CO ER) to multi-carbon chemical feedstocks over Cu-based catalysts is of considerable attraction but suffers with the ambiguous nature of active sites, which hinder the rational design of catalysts and large-scale industrialization. This paper describes a large-scale simulation to obtain realistic CuZn nanoparticle models and the atom-level structure of active sites for C products on CuZn catalysts in CO ER, combining neural network based global optimization and density functional theory calculations. Upon analyzing over 2000 surface sites through high throughput tests based on NN potential, two kinds of active sites are identified, balanced Cu-Zn sites and Zn-heavy Cu-Zn sites, both facilitating C-C coupling, which are verified by subsequent calculational and experimental investigations.

Ultrasound-Assisted Natural Deep Eutectic Solvent Extraction and Bioactivities of Flavonoids in Leaves.

We performed ultrasound-assisted extraction coupled with natural deep eutectic solvents (NADES) to achieve the green and efficient preparation of flavonoid extract from leaves. We then evaluated its antioxidant and antiproliferative activities. A NADES consisting of choline chloride and glucose at a molar ratio of 4:1 with 20% water was determined to be the most suitable solvent.

Enhanced entomopathogenic nematode yield and fitness via addition of pulverized insect powder to solid media.

Beneficial nematodes are used as biological control agents. Low-cost mass production of entomopathogenic nematodes (EPNs) is an important prerequisite toward their successful commercialization. EPNs can be grown via methods or in sold or liquid fermentation.