Antisite defect unleashes catalytic potential in high-entropy intermetallics for oxygen reduction reaction.

Developing highly active, low-cost, and durable catalysts for efficient oxygen reduction reactions remain a challenge, hindering the commercial viability of proton exchange membrane fuel cells (PEMFCs). In this study, an ordered PtZnFeCoNiCr high-entropy intermetallic electrocatalyst with Pt antisite point defects (PD-PZFCNC-HEI) is synthesized. The electrocatalyst shows high mass activity of 4.

PAM4-modulated 2-micron band for enhanced indoor optical communications: experimental evaluation.

This research explores the application of PAM4 modulation to optical signals in the 2-μm wavelength band for indoor optical communication. Experiments conducted in a simulated atmospheric turbulence environment demonstrated a BER of 10 for the 2-μm laser carrier with PAM4, surpassing the forward error correction threshold. Power loss comparisons to a back-to-back setup showed minimal degradation under various turbulence levels, with losses of 1.

A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites.

The exceptional properties of two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) have led to a rapid increase in the number of low-dimensional materials for optoelectronic engineering and solar energy conversion. The flexibility and controllability of 2D HOIPs create a vast structural space, which presents an urgent issue to effectively explore 2D HOIPs with better performance for practical applications. However, the traditional RP-DJ classification method falls short in describing the influence of structure on the electronic properties of 2D HOIPs.

Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices.

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance.

Single atom modified two-dimensional bismuthenes for toxic gas detection.

Accurate detection of toxic gases at low concentrations is often difficult because they are colorless, odorless, flammable and denser than air. Therefore, it is urgent to develop highly stable and sensitive toxic gas detectors. However, most gas sensors operate at high temperatures, making the detection of toxic gases more challenging.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins.

Encapsulating Metal-Organic-Framework Derived Nanocages into a Microcapsule for Shuttle Effect-Suppressive Lithium-Sulfur Batteries.

Long-term stable secondary batteries are highly required. Here, we report a unique microcapsule encapsulated with metal organic frameworks (MOFs)-derived CoO nanocages for a Li-S battery, which displays good lithium-storage properties. ZIF-67 dodecahedra are prepared at room temperature then converted to porous CoO nanocages, which are infilled into microcapsules through a microfluidic technique.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient.

An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites.

Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼10 faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.

A novel free-standing metal organic frameworks-derived cobalt sulfide polyhedron array for shuttle effect suppressive lithium-sulfur batteries.

Metal-organic-frameworks-derived nanostructures have received broad attention for secondary batteries. However, many strategies focus on the preparation of dispersive materials, which need complicated steps and some additives for making electrodes of batteries. Here, we develop a novel free-standing CoSpolyhedron array derived from ZIF-67, which grows on a three-dimensional carbon cloth for lithium-sulfur (Li-S) battery.

Sinoacutine inhibits inflammatory responses to attenuates acute lung injury by regulating NF-κB and JNK signaling pathways.

Background: Stephania yunnanensis H. S. Lo is widely used as an antipyretic, analgesic and anti-inflammatory herbal medicine in SouthWest China.

Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery.

The shuttle effect has been a major obstacle to the development of lithium-sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB-type sulfur host material to suppress the shuttle effect using the database, discovering 14 new structures (PdN, TaS, PtN, TaSe, AgCl, NbSe, TaTe, AgF, NiN, AuS, TmI, NbTe, NiBi, and AuBr) from 1320 AB-type compounds.

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression.

State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption.