The Moon has been bathed in solar wind for billions of years, but the two hemispheres are struck by solar wind of different speeds and energies. Now, research based on China’s Chang’e-6 samples reveals that Earth’s magnetosphere has shaped this difference. The study was published in Nature Geoscience.

Transition metal-based electrocatalysts, especially NiFe (oxy)hydroxides, represent state-of-the-art systems for the alkaline oxygen evolution reaction (OER). While structural evolution of electrocatalysts under working conditions has garnered considerable attention, as the evolved structure often dictates catalytic activity, the mechanisms driving these transformations and catalytic performance remain poorly understood. In this work, using NiFe phosphide (NiFeP) as a model system, we systematically elucidate how phosphorus guides irreversible structural transformations and boosts OER performance. Through a combination of electron microscopy, spectroscopy, and electrochemical analysis, we demonstrate that phosphorus facilitates the transformation of NiFeP into NiFe (oxy)hydroxides while retaining residual oxyanion moieties. Importantly, we reveal that phosphorus not only accelerates the active phase formation but also suppresses Fe dissolution and directly modulates the electronic structure, a dual function that has not been well-documented in the literature. The reconstructed catalyst exhibits outstanding OER activity and remarkable stability under alkaline conditions, achieving a low overpotential of 225 mV at 10 mA cm⁻² and stably maintaining a current density of 500 mA cm⁻² for over 100 h, ranking among the most effective OER catalysts reported in the literature. Density functional theory calculations indicated that the resulting PO₄³⁻ and Fe act synergistically to regulate the redox chemistry of Ni, preventing Ni over-oxidation and enhancing electronic conductivity. Furthermore, PO₄³⁻ stabilizes key OER intermediates and lower the thermodynamic energy barrier, thereby improving catalytic performance.

A series of layer-by-layer organic photovoltaics (LOPVs) were constructed using D18 as the donor and L8-BO, featuring exciton self-dissociation characteristics, as acceptor. A trace amount of high crystallinity, high-hole-mobility polymer P66 was intentionally introduced into the L8-BO layer to enhance the hole transport. The power conversion efficiency (PCE) of the LOPVs improved from 18.97% to 19.81% upon the addition of 0.005 wt.% P66 to the L8-BO layer, originating from the concurrent increases in short-circuit current density from 26.87 to 27.72 mA/cm2 and fill factor from 78.61% to 79.39%. The introduction of P66 into the L8-BO layer forms an efficient hole-transport network, promoting the transport of holes generated by exciton self-dissociation in L8-BO. In addition, introducing P66 optimizes molecular packing, thereby enhancing charge extraction and transport within active layers. The universality of incorporating P66 into acceptor layers is further demonstrated in a series of LOPVs with different acceptors. This work indicates that introducing materials with high hole mobility and crystallinity into the acceptor layer is a promising strategy for boosting the performance of LOPVs.

Thermal energy systems (TES) are an essential part of industries that have evolved over time through the engagement of managers and researchers. The development of digital twin (DT) technology has enabled accurate prediction of their performance. The inherent limitations of complex thermal systems, such as noisy input data and occasional lack of measurement data or boundary conditions, have recently created opportunities to apply physics-based problem-solving alongside DT technology. This paper aims to systematically review the novel physics-informed neural network-digital twin (PINN-DT) methodology as a potential solution to these challenges, and to present a taxonomy for problem-solving. The outcome of this study provides valuable guidance in selecting PINN-DT technology in thermal energy system (TES) modeling. A review of the proposed loss functions demonstrates that their design is critical for achieving precise outcomes in this technology, effectively serving as the foundational core of PINN-DT. As a result, it is recommended that the construction of the loss function be fundamentally guided by two principal considerations: forecasting accuracy and compliance with physical principles, which serve as foundational pillars in the surrogate model design framework. A significant gap exists in applying this technology to industries that use discrete sampling for quality control. Implementing the PINN-DT framework could address this issue by determining optimal sampling intervals, thereby offering vital decision-making support. Moreover, the absence of exergy analysis in formulating the physical loss component of the loss function represents a significant research gap. Future studies should therefore incorporate the exergy concept into the design of the loss function.

A research team has developed HerbSyner_Finder, a network community-based computational model that can identify synergistic ingredient combinations from herbal medicines and other complex biological systems.