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Thermal energy storage offers enormous potential for a wide range of energy technologies. Phase-change materials offer state-of-the-art thermal storage due to high latent heat. However, spontaneous heat loss from thermally charged phase-change materials to cooler surroundings occurs due to the absence of a significant energy barrier for the liquid–solid transition. This prevents control over the thermal storage, and developing effective methods to address this problem has remained an elusive goal. Herein, we report a combination of photo-switching dopants and organic phase-change materials as a way to introduce an activation energy barrier for phase-change materials solidification and to conserve thermal energy in the materials, allowing them to be triggered optically to release their stored latent heat. This approach enables the retention of thermal energy (about 200 J g −1 ) in the materials for at least 10 h at temperatures lower than the original crystallization point, unlocking opportunities for portable thermal energy storage systems. Phase-change materials offer excellent thermal storage due to their high latent heat; however, they suffer from spontaneous heat loss. Han et al., use organic photo-switching dopants to introduce an activation energy barrier which enables controllable thermal energy release and retention.

Optical phase change materials (O-PCMs), a unique group of materials featuring exceptional optical property contrast upon a solid-state phase transition, have found widespread adoption in photonic applications such as switches, routers and reconfigurable meta-optics. Current O-PCMs, such as Ge–Sb–Te (GST), exhibit large contrast of both refractive index (Δ n ) and optical loss (Δ k ), simultaneously. The coupling of both optical properties fundamentally limits the performance of many applications. Here we introduce a new class of O-PCMs based on Ge–Sb–Se–Te (GSST) which breaks this traditional coupling. The optimized alloy, Ge 2 Sb 2 Se 4 Te 1 , combines broadband transparency (1–18.5 μm), large optical contrast (Δ n  = 2.0), and significantly improved glass forming ability, enabling an entirely new range of infrared and thermal photonic devices. We further demonstrate nonvolatile integrated optical switches with record low loss and large contrast ratio and an electrically-addressed spatial light modulator pixel, thereby validating its promise as a material for scalable nonvolatile photonics. Here, the authors introduce optical phase change materials based on Ge-Sb-Se-Te which breaks the coupling between refractive index and optical loss allowing low-loss performance benefits. They demonstrate low losses in nonvolatile photonic circuits and electrical pixelated switching have been demonstrated.

Controlling the flow of thermal energy is crucial to numerous applications ranging from microelectronic devices to energy storage and energy conversion devices. Here, we report ultralow lattice thermal conductivities of solution-synthesized, single-crystalline all-inorganic halide perovskite nanowires composed of CsPbI₃ (0.45 ± 0.05 W·m−1·K−1), CsPbBr₃ (0.42 ± 0.04 W·m−1·K−1), and CsSnI₃ (0.38 ± 0.04 W·m−1·K−1). We attribute this ultralow thermal conductivity to the cluster rattling mechanism, wherein strong optical–acoustic phonon scatterings are driven by a mixture of 0D/1D/2D collective motions. Remarkably, CsSnI₃ possesses a rare combination of ultralow thermal conductivity, high electrical conductivity (282 S·cm−1), and high hole mobility (394 cm²·V−1·s−1). The unique thermal transport properties in all-inorganic halide perovskites hold promise for diverse applications such as phononic and thermoelectric devices. Furthermore, the insights obtained from this work suggest an opportunity to discover low thermal conductivity materials among unexplored inorganic crystals beyond caged and layered structures.

Objective Diet habit plays a vital role in fecal incontinence (FI) progression. However, it remains unknown whether dietary quality is related to FI. Our study sought to explore the relationship between healthy eating index-2015 (HEI-2015) score and FI among US adults. Methods An analysis of data from the 2005–2010 National Health and Nutrition Examination Survey was conducted in our study. The Bowel Health Questionnaire defined FI as losing liquid, solid, or mucus stool at least monthly. The diet’s quality was evaluated using HEI-2015 score. The odds ratios (ORs) and 95% confidence interval (95%CI) were calculated using multi-variable logistic regression models. Results There were 11,452 participants, with 9.3% (1062/11452) who experienced FI. Compared with individuals with inadequate group (HEI score < 50), the adjusted OR values for HEI score and FI in average group (50 ≤ HEI score < 70) and optimal group (HEI score ≥ 70) were 0.89 (95%CI: 0.74–1.07, p  = 0.214) and 0.69 (95%CI: 0.52–0.91, p  = 0.011), respectively. Subsequent stratified analyses did not reveal any interactions. Conclusions High-quality diets are related with a lower risk of FI. Therefore, it is imperative to take into account the potential impact of diet on FI when devising strategies for the treatment and prevention.

Purpose This paper aims to explore the performance implications of supply chain integration (SCI) taking a strategic perspective. Thus, this paper is set to provide answers to the following research questions: Does a higher degree of SCI always lead to greater firm performance improvements? As the answer to this question is likely to be no, the authors explore the performance implications from a strategic perspective: Is the SCI–performance relationship contingent on a company’s competitive priorities (i.e. operations strategy)? Design/methodology/approach The authors explore their questions through multiple quasi-independent data sets to test the impact of SCI on firm performance. Furthermore, the authors provide a more nuanced conceptual and empirical view to explore the previously uncovered contradictory results and contingent relationship challenging the “more integration equals higher firm performance” proposition. Findings The results only provide partial support for the proposition that more integration is always beneficial in the supply chain context. The authors also identified that the impact of SCI on financial performance is contingent on a company’s competitive priorities. Originality/value This study provides a much-needed comprehensive assessment of the SCI–performance relationship through critically re-evaluating one of the most popular propositions in the field of supply chain management. The results can be extrapolated beyond the dyad, as the authors conceptualise integration simultaneously from an upstream and downstream perspective.

Learning the global crystal symmetry and interpreting the equivariant information is crucial for accurately predicting material properties, yet remains to be fully accomplished by existing algorithms based on convolution networks. To overcome this challenge, here we develop a machine learning (ML) model, named symmetry-enhanced equivariance network (SEN), to build material representation with joint structure-chemical patterns, to encode important clusters embedded in the crystal structure, and to learn pattern equivariance in different scales via capsule transformers. Quantitative analyses of the intermediate matrices demonstrate that the intrinsic crystal symmetries and interactions between clusters have been exactly perceived by the SEN model and critically affect the prediction performances by reducing effective feature space. The mean absolute errors (MAEs) of 0.181 eV and 0.0161 eV/atom are obtained for predicting bandgap and formation energy in the MatBench dataset. The general and interpretable SEN model reveals the potential to design ML models by implicitly encoding feature relationship based on physical mechanisms. Learning global crystal symmetry and interpreting equivariance are crucial for developing ML model to predict electronic properties. Here authors propose a symmetry-enhanced model to simulate cluster interactions and to predict materials properties.

The transparency of two-dimensional (2D) materials to intermolecular interactions of crystalline materials has been an unresolved topic. Here we report that remote atomic interaction through 2D materials is governed by the binding nature, that is, the polarity of atomic bonds, both in the underlying substrates and in 2D material interlayers. Although the potential field from covalent-bonded materials is screened by a monolayer of graphene, that from ionic-bonded materials is strong enough to penetrate through a few layers of graphene. Such field penetration is substantially attenuated by 2D hexagonal boron nitride, which itself has polarization in its atomic bonds. Based on the control of transparency, modulated by the nature of materials as well as interlayer thickness, various types of single-crystalline materials across the periodic table can be epitaxially grown on 2D material-coated substrates. The epitaxial films can subsequently be released as free-standing membranes, which provides unique opportunities for the heterointegration of arbitrary single-crystalline thin films in functional applications.

Ulcerative colitis (UC), as an intractably treated disease, seriously affects the quality of life of patients and has an increase in terms of incidence and prevalence annually. However, due to the lack of a direct etiology and drug-induced side effects, the medical treatment of UC falls into a bottleneck. There are many natural phytochemicals with the potential to regulate immune function in nature. Herein, a potential mechanism of artemisinin in the treatment of UC and potential druggability compounds with an artemisinin peroxide bond were discussed and predicted based on computer-aided drug design (CADD) technology by using the methods of network pharmacology, molecular docking, de novo drug structure design and molecular dynamics through the integration of artemisinin related targets from TCMSP, ChEMBL and HERB databases. The networks were constructed based on 50 artemisinin-disease intersection targets related to inflammation, cytokines, proliferation and apoptosis, showing the importance of GALNT2, BMP7 and TGFBR2 in the treatment of disease, which may be due to the occupation of the ricin B-type lectin domain of GALNT2 by artemisinin compounds or de novo designed candidates. This result could guide the direction of experiments and actual case studies in the future. This study provides a new route for the application of artemisinin and the development of drugs.

The spectral properties are the most prevalent continuous representation for characterizing transport phenomena and excitation responses, yet their accurate predictions remain a challenge due to the inability to perceive series correlations by existing machine learning (ML) models. Herein, a ML model named cluster‐based series graph networks (CSGN) is developed based on the dynamical theory of crystal lattices to predict phonon density of states (PDOS) spectrum for crystal materials. The multiple atomic cluster representation is constructed to capture the diverse vibration modes, while the mixture Gaussian process and dynamic time warping mechanism are compiled to project from clusters to PDOS spectrum. Accurate predictions of complicated spectra with multiple or overlapping peaks are achieved. The high performance of CSGN model can be attributed to the pertinent feature extraction and the appropriate similarity evaluation, which enable the natural perception of structure‐property relation and intrinsic series correlations as confirmed in the predictive results. The transferable and interpretable CSGN model advances ML predictions of spectral properties and reveals the potential of designing ML methods based on physical mechanisms. A machine learning model (CSGN) based on the cluster graph and mixture Gaussian process is developed to predict phonon density of states (PDOS) spectrum for crystal materials. Accurate predictions of complicated spectra with multiple or overlap peaks are achieved, owing to the intelligent perception of structure‐property relation and intrinsic series correlations.

The discovery and optimization of functional nanocomposites can be potentially accomplished by joint ab-initio and machine learning (ML) exploitation, which is currently hindered by the absence of an ML model to appropriately describe intermonomer interactions in atomic scale. We developed a deep learning model named double-region network (DRN) to fill this gap via simultaneously learning multi-scale interactions. An ultra-low mean absolute error of 2.8 meV is achieved to predict electronic couplings of 337 distinct molecule types in random configurations, with a tiny training set of 21 configurations per molecule type. The hierarchical material representation based on atomic chemical environments with small and large cutoff radii is demonstrated to be crucial for the high transferability and robustness of the DRN model. Such representation not only captures the local features of conjugated fragments, but also encodes the important intermolecular fragment interactions prior to model training. The ML model established in this study offers a general framework for describing intermonomer interactions and opens an opportunity for the inverse design of complex nanocomposites.