Conventional photovoltaic-thermal (PV-T) collectors have coupled electrical and thermal outputs, limiting the temperature of the delivered thermal energy typically to < 60 °C. Concentrating PV-T (CPV-T) collector designs can reach higher temperatures, but cell overheating from similar coupling limitations reduces their electrical efficiency. Spectral splitting—dividing the solar spectrum so that only useful wavelengths reach the PV cells—promises to break this compromise, yet to go beyond previous studies and propose advanced spectral-splitting CPV-T designs capable of breakthrough performance, it is necessary to develop fully coupled optical, electrical and thermal-fluid models validated at the collector scale.

With the development of the Internet and intelligent education systems, the significance of cognitive diagnosis has become increasingly acknowledged. Cognitive diagnosis models (CDMs) aim to characterize learners’ cognitive states based on their responses to a series of exercises. However, conventional CDMs often struggle with less frequently observed learners and items, primarily due to limited prior knowledge. Recent advancements in large language models (LLMs) offer a promising avenue for infusing rich domain information into CDMs. However, integrating LLMs directly into CDMs poses significant challenges. While LLMs excel in semantic comprehension, they are less adept at capturing the fine-grained and interactive behaviours central to cognitive diagnosis. Moreover, the inherent difference between LLMs’ semantic representations and CDMs’ behavioural feature spaces hinders their seamless integration. To address these issues, this research proposes a model-agnostic framework to enhance the knowledge of CDMs through LLMs extensive knowledge. It enhances various CDM architectures by leveraging LLM-derived domain knowledge and the structure of observed learning outcomes taxonomy. It operates in two stages: first, LLM diagnosis, which simultaneously assesses learners via educational techniques to establish a richer and a more comprehensive knowledge representation; second, cognitive level alignment, which reconciles the LLM’s semantic space with the CDM’s behavioural domain through contrastive learning and mask-reconstruction learning. Empirical evaluations on multiple real-world datasets demonstrate that the proposed framework significantly improves diagnostic accuracy and underscoring the value of integrating LLM-driven semantic knowledge into traditional cognitive diagnosis paradigms. 

Researchers at the Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, report in ACS Nano the successful creation of artificial synaptic vesicles that can be remotely controlled by near-infrared (NIR) light. By embedding a phthalocyanine dye into lipid bilayers, the team achieved local heating that modulates membrane permeability, enabling precise release of neurotransmitters such as acetylcholine. These findings demonstrate that nanoscale heating can control communication between nerve cells. The work opens new avenues for non-genetic modulation of neuronal activity, with potential applications in neuroscience, drug delivery, and bioengineering.

Carbon dioxide energy storage (CES) is an emerging compressed gas energy storage technology which offers high energy storage efficiency, flexibility in location, and low overall costs. This study focuses on a CES system that incorporates a high-temperature graded heat storage structure, utilizing multiple heat exchange working fluids. Unlike traditional CES systems that utilize a single thermal storage at low to medium temperatures, this system significantly optimizes the heat transfer performance of the system, thereby improving its cycle efficiency. Under typical design conditions, the round-trip efficiency of the system is found to be 76.4%, with an output power of 334 kW/(kg·s−1) per unit mass flow rate, through mathematical modeling. Performance analysis shows that increasing the total pressure ratio, reducing the heat transfer temperature difference, improving the heat exchanger efficiency, and lowering the ambient temperature can enhance cycle efficiency. Additionally, this paper proposes a universal and theoretical CES thermodynamic intrinsic cycle construction method and performance prediction evaluation method for CES systems, providing a more standardized and accurate approach for optimizing CES system design.

Electrocatalytic nitric oxide (NO) reduction reaction (NORR) is a promising and sustainable process that can simultaneously realize green ammonia (NH₃) synthesis and hazardous NO removal. However, current NORR performances are far from practical needs due to the lack of efficient electrocatalysts. Engineering the lattice of metal-based nanomaterials via phase control has emerged as an effective strategy to modulate their intrinsic electrocatalytic properties. Herein, we realize boron (B)-insertion-induced phase regulation of rhodium (Rh) nanocrystals to obtain amorphous Rh₄B nanoparticles (NPs) and hexagonal close-packed (hcp) RhB NPs through a facile wet-chemical method. A high Faradaic efficiency (92.1 ± 1.2%) and NH₃ yield rate (629.5 ± 11.0 µmol h⁻¹ cm⁻²) are achieved over hcp RhB NPs, far superior to those of most reported NORR nanocatalysts. In situ spectro-electrochemical analysis and density functional theory simulations reveal that the excellent electrocatalytic performances of hcp RhB NPs are attributed to the upshift of d-band center, enhanced NO adsorption/activation profile, and greatly reduced energy barrier of the rate-determining step. A demonstrative Zn–NO battery is assembled using hcp RhB NPs as the cathode and delivers a peak power density of 4.33 mW cm⁻², realizing simultaneous NO removal, NH₃ synthesis, and electricity output.

Using EUROSTAT data and double randomization, the co-led study improves the Benefit of the Doubt model through a novel Ensemble-DEA framework that mitigates the curse of dimensionality in SDG indicators. Published in Expert Systems with Applications, the method offers more reliable EU performance rankings and benchmarking tools for evaluating sustainability policies across member states.