The nonisothermal crystallization kinetics of poly(propylene) (PP) and poly(propylene)/organic‐montmorillonite (PP/Mont) nanocomposite were investigated by differential scanning calorimetry (DSC) with various cooling rates. The Avrami analysis modified by previous research was used to describe the nonisothermal crystallization process of PP and PP/Mont nanocomposite very well. The values of half‐time and Zc showed that the crystallization rate increased with increasing cooling rates for both PP and PP/Mont nanocomposite, but the crystallization rate of PP/Mont nanocomposite was faster than that of PP at a given cooling rate. The activation energies were estimated by the Kissinger method, and the values were 189.4 and 155.7 kJ/mol for PP and PP/Mont nanocomposite, respectively. PP/Mont nanocomposite could be easily fabricated as original PP, although the addition of organomontmorillonite might accelerate the overall nonisothermal crystallization process. © 2002 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 40: 408–414, 2002; DOI 10.1002/polb.10101

Conventional machines rely on rigid, centralized electronic components to make decisions, which limits complexity and scaling. Here, we show that decision making can be realized on the material-level without relying on semiconductor-based logic. Inspired by the distributed decision making that exists in the arms of an octopus, we present a completely soft, stretchable silicone composite doped with thermochromic pigments and innervated with liquid metal. The ability to deform the liquid metal couples geometric changes to Joule heating, thus enabling tunable thermo-mechanochromic sensing of touch and strain. In more complex circuits, deformation of the metal can redistribute electrical energy to distal portions of the network in a way that converts analog tactile ‘inputs’ into digital colorimetric ‘outputs’. Using the material itself as the active player in the decision making process offers possibilities for creating entirely soft devices that respond locally to environmental interactions or act as embedded sensors for feedback loops.

ABSTRACT:The nonisothermal crystallization kinetics of polyoxymethylene (POM), polyoxymethylene/Na-montmorillonite (POM/Na-MMT), and polyoxymethylene/organic-montmorillonite (POM/organ-MMT) nanocomposites were investigated by differential scanning calorimetry at various cooling rates. The Avrami analysis modified by Jeziorny and a method developed by Mo were employed to describe the nonisothermal crystallization process of POM/Na-MMT and POM/organ-MMT nanocomposites. The difference in the values of the exponent n between POM and POM/montmorillonite nanocomposites suggests that the nonisothermal crystallization of POM/Na-MMT and POM/organ-MMT nanocomposites corresponds to a tridimensional growth with heterogeneous nucleation. The values of half-time and the parameter Z c , which characterizes the kinetics of nonisothermal crystallization, show that the crystallization rate of either POM/Na-MMT or POM/organ-MMT nanocomposite is faster than that of virgin POM at a given cooling rate. The activation energies were evaluated by the Kissinger method and were 387.0, 330.3, and 328.6 kJ/mol for the nonisothermal crystallization of POM, POM/Na-MMT nanocomposite, and POM/organ-MMT nanocomposite, respectively. POM/montmorillonite nanocomposite can be as easily fabricated as the original polyoxymethylene, considering that the addition of montmorillonite, either Na-montmorillonite or organ-montmorillonite, may accelerate the overall nonisothermal crystallization process.

Luminescent materials have become prevalent in data communication and information security because of their special optical characteristics. Conventional luminescent materials generally exhibit unicolor emission and fixed excitation mode, resulting in decreased efficiency of anticounterfeiting applications. The development of an iridescent chameleon-like material that can change its emission color under different stimulations is a significant challenge. Here, we propose that Pb2+, Mn2+, and lanthanide cations (such as Y3+, Tb3+, Yb3+) co-doped in Na2CaGe2O6 particles can be an effective tool for designing dual-mode anticounterfeiting materials based on their tunable fluorescence/persistent luminescence transformation and excitation wavelength-dependent emission. Ultimately, a proof-of-concept anticounterfeiting fabric is obtained by using the as-prepared phosphors and exhibits a dynamic multiple color response. This work exploits the possibility of developing a new class of multimode anticounterfeit materials, which would be almost impossible to mimic or counterfeit, providing a very high level of security.

BackgroundLoss of a sensory function is often followed by the hypersensitivity of other modalities in mammals, which secures them well-awareness to environmental changes. Cellular and molecular mechanisms underlying cross-modal sensory plasticity remain to be documented.Methodology/Principal FindingsMultidisciplinary approaches, such as electrophysiology, behavioral task and immunohistochemistry, were used to examine the involvement of specific types of neurons in cross-modal plasticity. We have established a mouse model that olfactory deficit leads to a whisking upregulation, and studied how GABAergic neurons are involved in this cross-modal plasticity. In the meantime of inducing whisker tactile hypersensitivity, the olfactory injury recruits more GABAergic neurons and their fine processes in the barrel cortex, as well as upregulates their capacity of encoding action potentials. The hyperpolarization driven by inhibitory inputs strengthens the encoding ability of their target cells.Conclusion/SignificanceThe upregulation of GABAergic neurons and the functional enhancement of neuronal networks may play an important role in cross-modal sensory plasticity. This finding provides the clues for developing therapeutic approaches to help sensory recovery and substitution.

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Background Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onset and progression based on color fundus photographs (CFPs), with clinical validation of performance in external population cohorts. Methods We established data sets of CFPs and visual fields collected from longitudinal cohorts. The mean follow-up duration was 3 to 5 years across the data sets. Artificial intelligence (AI) models were developed to predict future glaucoma incidence and progression based on the CFPs of 17,497 eyes in 9346 patients. The area under the receiver operating characteristic (AUROC) curve, sensitivity, and specificity of the AI models were calculated with reference to the labels provided by experienced ophthalmologists. Incidence and progression of glaucoma were determined based on longitudinal CFP images or visual fields, respectively. Results The AI model to predict glaucoma incidence achieved an AUROC of 0.90 (0.81–0.99) in the validation set and demonstrated good generalizability, with AUROCs of 0.89 (0.83–0.95) and 0.88 (0.79–0.97) in external test sets 1 and 2, respectively. The AI model to predict glaucoma progression achieved an AUROC of 0.91 (0.88–0.94) in the validation set, and also demonstrated outstanding predictive performance with AUROCs of 0.87 (0.81–0.92) and 0.88 (0.83–0.94) in external test sets 1 and 2, respectively. Conclusion Our study demonstrates the feasibility of deep-learning algorithms in the early detection and prediction of glaucoma progression. FUNDING National Natural Science Foundation of China (NSFC); the High-level Hospital Construction Project, Zhongshan Ophthalmic Center, Sun Yat-sen University; the Science and Technology Program of Guangzhou, China (2021), the Science and Technology Development Fund (FDCT) of Macau, and FDCT-NSFC.