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Free-standing electrode materials provide many desirable properties for electrochemical energy storage devices due to their light weight, good conductive capacity, excellent mechanical strength, high energy/power density and extraordinary electrochemical stability. Particularly, carbonaceous matrix nanomaterials, such as graphene materials, carbon nanotubes, carbon nanofibers, carbon papers and carbon cloths, play important roles in the free-standing electrodes, including serving as conducting network skeleton, loading electrochemically active material, enhancing mechanical toughness and flexibility, and preventing the structural damage during charge/discharge processes. In this review, we give a systematic overview of the state-of-the-art research progress on carbonaceous matrixes-based free-standing electrode materials for electrochemical energy storage, from synthesis methods, structural design, to important applications in flexible energy storage devices including lithium-ion batteries, lithium-sulfur batteries, sodium-ion batteries, lithium-oxygen batteries, and supercapacitors for each class of matrix-based electrode materials. In particular, the structure design strategies utilizing the advantages of free-standing matrixes to address the existing issues and improve the electrochemical and mechanical performance of energy storage devices are discussed in detail. At the end, we also discuss the challenges and demonstrate the prospective for the future development of such materials for advanced flexible energy storage devices.

Introduction: The typical functionality of astrocytes was previously shown to be disrupted by Parkinson's disease (PD), which actively regulates synaptic neurotransmission. However, the morphological changes in astrocytes wrapping glutamatergic synapses in the striatum after dopamine (DA) neuronal degeneration is unclear. Methods: We utilized a range of methodologies, encompassing the 6-hydroxydopamine (6OHDA)-induced PD model, as well as techniques such as immunohistochemistry, Western blotting, immunofluorescence and immunoelectron microscopy (IEM) to delve into the consequences of DA neuronal degeneration on the morphological attributes of perisynaptic astrocytes. Results: Our findings demonstrated a notable rise in glial fibrillary acidic protein (GFAP) + astrocyte density and an upregulation in GFAP protein expression within the striatum due to DA neuronal degeneration, coincided with the enlargement, elongation, and thickening of astrocyte protuberances. However, the expression levels of glutamate transporter 1 (GLT1) and glutamine synthetase (GS), which are related to glutamate–glutamine cycle, were significantly reduced. Double immunofluorescence and IEM results indicated that different proportions of vesicular glutamate transporter 1 (VGlut1)+ and vesicular glutamate transporter 2 (VGlut2) + terminals were wrapped by astrocytes. Additionally, DA neuronal degeneration increased the percentage and area of VGlut1+ and VGlut2+ terminals wrapped by GFAP + astrocytes in the striatum. Furthermore, we noted that DA neuronal degeneration increased the percentage of VGlut1+ and VGlut2+ axo-spinous synapses wrapped by astrocytes but had no effect on axo-dendritic synapses. Conclusion: Hence, perisynaptic astrocytes wrapping striatal glutamatergic synapses exhibit substantial morphological and functional alterations following DA neuronal degeneration making them a potential target for therapeutic interventions in PD.

The assessment of energy performance in smart buildings has emerged as a prominent area of research driven by the increasing energy consumption trends worldwide. Analyzing the attributes of buildings using optimized machine learning models has been a highly effective approach for estimating the cooling load (CL) and heating load (HL) of the buildings. In this study, an artificial neural network (ANN) is used as the basic predictor that undergoes optimization using five metaheuristic algorithms, namely coati optimization algorithm (COA), gazelle optimization algorithm (GOA), incomprehensible but intelligible-in-time logics (IbIL), osprey optimization algorithm (OOA), and sooty tern optimization algorithm (STOA) to predict the CL and HL of a residential building. The models are trained and tested via an Energy Efficiency dataset (downloaded from UCI Repository). A score-based ranking system is built upon three accuracy evaluators including mean absolute percentage error (MAPE), root mean square error (RMSE), and percentage-Pearson correlation coefficient (PPCC) to compare the prediction accuracy of the models. Referring to the results, all models demonstrated high accuracy (e.g., PPCCs >89%) for predicting both CL and HL. However, the calculated final scores of the models (43, 20, 39, 38, and 10 in HL prediction and 36, 20, 42, 42, and 10 in CL prediction for the STOA, OOA, IbIL, GOA, and COA, respectively) indicated that the GOA, IbIL, and STOA perform better than COA and OOA. Moreover, a comparison with various algorithms used in earlier literature showed that the GOA, IbIL, and STOA provide a more accurate solution. Therefore, the use of ANN optimized by these three algorithms is recommended for practical early forecast of energy performance in buildings and optimizing the design of energy systems.

A novel green pectin-driven sol-gel with an auto-combustion route was successfully applied for the synthesis of manganese oxide catalysts, which exhibited superior catalytic performance for low-temperature combustion of toluene compared to those cases using manganese oxide catalysts synthesized by traditional citric acid and glycine-driven sol-gel routes. The characterization results showed that the MnOx-P catalyst synthesized by the present method exhibited a unique hierarchical mesoporous structure with lower crystallinity and smaller grain size, leading to abundant active species and defect structures. Comparing with the catalysts synthesized by those traditional sol-gel methods, the MnOx-P catalyst had excellent low-temperature reducible performance and better oxygen mobility, which is considered to be resulted from the specific interaction of chelation and calcination processes. In addition, the MnOx-P catalyst exhibited satisfactory long-term stability and water tolerance during catalytic toluene combustion process.

Hyperthyroidism has been identified as a risk factor for cognitive disorders. The hippocampus is a key brain region associated with cognitive function, among which excitatory synapse transmission plays an important role in the process of learning and memory. However, the mechanism by which hyperthyroidism leads to cognitive dysfunction through a synaptic mechanism remains unknown. We investigated the synaptic mechanisms in the effects of hyperthyroidism in an animal model that involved repeated injection of triiodothyronine (T3). These mice displayed impaired learning and memory in the Novel object recognition test, Y-maze test, and Morris Water Maze test, as well as elevated anxiety in the elevated plus maze. Mature dendritic spines in the hippocampal CA1 region of hyperthyroid mice were significantly decreased, accompanied by decreased level of AMPA- and NMDA-type glutamate receptors in the hippocampus. In primary cultured hippocampal neurons, levels of AMPA- and NMDA-type glutamate receptors also decreased and whole-cell patch-clamp recording revealed that excitatory synaptic function was obviously attenuated after T3 treatment. Notably, pharmacological activation of AMPAR or NMDAR by intraperitoneal injection of CX546, an AMPAR agonist, or NMDA, an NMDAR agonist can restore excitatory synaptic function and corrected impaired learning and memory deficit in hyperthyroid mice. Together, our findings uncovered a previously unrecognized AMPAR and NMDAR-dependent mechanism involved in regulating hippocampal excitatory synaptic transmission and learning and memory disorders in hyperthyroidism.