Solar-driven interfacial evaporation has emerged as a highly promising route, leveraging abundant solar energy for efficient desalination. However, many existing solar evaporators still face challenges such as salt accumulation and, crucially, fail to reduce heavy metal concentrations to the ultra-low levels required by drinking water standards. To address this gap, we have developed a room-temperature fabricated graphene oxide/siloxane-PVA hydrogel (GO/SPH) evaporator wherein solar-driven evaporation works in synergy with integrated chelating amino groups to achieve the thorough removal of heavy metal ions concurrent with desalination. The optimized 3.0 wt% GO/SPH composite demonstrates a remarkable evaporation rate of 2.21 kg m- 2 h- 1 under 1 sun irradiation, a two-fold improvement over the pristine SPH. This exceptional dual-function performance is underpinned by its synergistic architecture. For solar evaporation, GO within the matrix acts as a highly efficient photothermal agent, achieving 91.4% conversion efficiency. Concurrently, for heavy metal remediation, amino ligands derived from the APTMS crosslinker enable the selective sequestration of Cu2+, Cr3+, Cd2+, and Pb2+ ions through specific coordination chemistry. Crucially, the entire evaporator is stabilized by a hydrolytically stable Si-O-Si network, which ensures robust multi-cycle performance by retaining over 95% of its initial efficiency and preserving its decontamination integrity over extended operation.
We study spectral moments of the Bures-Hall random matrices ensemble. The main result establishes a recurrence relation for the kth spectral moment valid for a real-valued k, in contrast to prevailing results in the literature of different ensembles of assuming an integer k. The key to establish the recurrence relation is the obtained Christoffel-Darboux formulae of correlation kernels of the ensemble that avoid tedious summations. As an application of our spectral moment results, we re-derive the formulae of average von Neumann entropy and quantum purity of Bures-Hall ensemble conjectured by Ayana Sarkar and Santosh Kumar.
Deep learning models are emerging as strong alternatives to numerical weather prediction, yet their internal representations remain poorly understood. We analyze the latent space of Microsoft’s Aurora model to test whether its embed- dings align with known physical processes. First, we show that land–sea distinctions are strongly captured, with errors mainly at coastlines. Second, we examine extreme surface temperatures using percentile-based thresholds, finding that embeddings reveal a gradient from moderate to severe events, though recall degrades at the rarest percentiles. These results suggest that Aurora’s encoder encodes physically consistent features but underestimates rare extremes. Our study combines deep learning forecasting, interpretable representation learning, and classical ML probing, illustrating how cross-disciplinary AI methods can yield insight into foundation models
The rapid advancement of generative artificial intelligence (GenAI) presents both opportunities and challenges for the Scholarship of Teaching and Learning (SoTL). This paper focuses on how SoTL scholars can thoughtfully integrate GenAI into their research processes while maintaining ethical integrity and preserving the essential human elements of scholarly inquiry. We critically examine GenAI’s capabilities, such as its potential to assist in brainstorming, to structure research ideas and protocols, and to expand methodological toolkits. However, we also emphasize its limitations, particularly regarding data reliability, inherent biases, and ethical concerns, such as transparency and student privacy. Through reflexive engagement with AI tools, we demonstrate how scholars can use GenAI as an intellectual partner rather than a substitute for critical thinking. We summarize our PRACTICE recommendations for use of GenAI in SoTL: Promote reflexivity; Read the literature; Act transparently; Conduct ethical research; Think critically; Invest in continuous learning; Check and validate outputs; Experiment and share. Ultimately, we argue that SoTL scholars must develop new competencies in order to navigate AI-enhanced research. While GenAI holds promise for advancing scholarly inquiry, its ethical use requires careful consideration and ongoing dialogue. By maintaining a balance between leveraging AI’s capabilities and upholding scholarly integrity, researchers can uphold SoTL practices that serve the field’s fundamental purposes: understanding, informing, and evolving teaching and learning for the benefit of students.
Spectral form factor (SFF), one of the key quantity from random matrix theory, serves as an important tool to probe universality in disordered quantum systems and quantum chaos. In this work, we present exact closed-form expressions for the second- and third-order SFFs in the circular unitary ensemble (CUE), valid for all real values of the time parameter, and analyze their asymptotic behavior in different regimes. In particular, for the second-order SFF, we derive an exact closed-form expression in terms of polygamma functions. In the limit of infinite matrix size, and when the time parameter is restricted to integer values, the second-order SFF reproduces the standard result established in earlier studies. When the time parameter is of order one relative to the matrix size, we demonstrate that the second-order SFF grows logarithmically with the ensemble dimension. For the third-order SFFs, a closed-form result in a special case is obtained by exploiting the translational invariance of CUE.
