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. 

A research team has identified Crocin II, a natural compound derived from saffron, as a promising therapeutic candidate for metabolic dysfunction-associated steatotic liver disease (MASLD). 

A research team has identified cytosine monophosphate kinase 2 (CMPK2) as a key molecular driver of hepatic ischemia-reperfusion injury (HIRI) and shown that acteoside (ACT), a natural compound found in traditional Chinese medicinal herbs, can protect the liver by interrupting CMPK2-centered redox metabolism. 

Tubular membrane protrusions — filopodia, tunneling nanotubes, retraction fibers — are ubiquitous structures on the cell surface, essential for intercellular communication, wound healing, angiogenesis, and cancer progression. Yet these delicate membrane tubes face a fundamental biophysical paradox: they must be flexible enough to perform dynamic functions, yet robust enough to withstand mechanical forces such as fluid shear stress and cell–matrix adhesion. While the cytoskeleton has long been recognized as the primary structural support for membrane tubules, the contribution of transmembrane proteins has remained largely unexplored. 

In this study, the authors reveal that the four-pass transmembrane protein tetraspanin 7 (TSPAN7) senses membrane curvature and spontaneously polymerizes into a highly ordered helical configuration on tubular membranes. This helical assembly acts as a "transmembrane skeleton" — a structural scaffold embedded within the lipid bilayer itself — that resists shear-force-induced tube narrowing and rupture. The concept of a transmembrane skeleton, operating not beneath but within the membrane, represents a previously unrecognized class of membrane skeletal architecture and opens new avenues for understanding how cells maintain the structural integrity of their most delicate membrane protrusions.