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Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed.

Construction of a solid electrolyte interphase (SEI) of zinc (Zn) electrode is an effective strategy to stabilize Zn electrode/electrolyte interface. However, single-layer SEIs of Zn electrodes undergo rupture and consequent failure during repeated Zn plating/stripping. Here, we propose the construction of a robust bilayer SEI that simultaneously achieves homogeneous Zn 2+ transport and durable mechanical stability for high Zn utilization rate (ZUR) and Coulombic efficiency (CE) of Zn electrode by adding 1,3-Dimethyl-2-imidazolidinone as a representative electrolyte additive. This bilayer SEI on Zn surface consists of a crystalline ZnCO 3 -rich outer layer and an amorphous ZnS-rich inner layer. The ordered outer layer improves the mechanical stability during cycling, and the amorphous inner layer homogenizes Zn 2+ transport for homogeneous, dense Zn deposition. As a result, the bilayer SEI enables reversible Zn plating/stripping for 4800 cycles with an average CE of 99.95% (± 0.06%). Meanwhile, Zn | |Zn symmetric cells show durable lifetime for over 550 h with a high ZUR of 98% under an areal capacity of 28.4 mAh cm −2 . Furthermore, the Zn full cells based on the bilayer SEI functionalized Zn negative electrodes coupled with different positive electrodes all exhibit stable cycling performance under high ZUR. The stabilization of the zinc electrode/electrolyte interface is critical for aqueous zinc battery operation. Here, authors report an electrolyte additive which promotes the formation of a bilayer solid electrolyte interphase, increasing the efficiency and utilization of zinc plating and stripping.

The development of Zn-free anodes to inhibit Zn dendrite formation and modulate high-capacity Zn batteries is highly applauded yet very challenging. Here, we design a robust two-dimensional antimony/antimony-zinc alloy heterostructured interface to regulate Zn plating. Benefiting from the stronger adsorption and homogeneous electric field distribution of the Sb/Sb 2 Zn 3 -heterostructured interface in Zn plating, the Zn anode enables an ultrahigh areal capacity of 200 mAh cm −2 with an overpotential of 112 mV and a Coulombic efficiency of 98.5%. An anode-free Zn-Br 2 battery using the Sb/Sb 2 Zn 3 -heterostructured interface@Cu anode shows an attractive energy density of 274 Wh kg −1  with a practical pouch cell energy density of 62 Wh kg −1 . The scaled-up Zn-Br 2 battery in a capacity of 500 mAh exhibits over 400 stable cycles. Further, the Zn-Br 2 battery module in an energy of 9 Wh (6 V, 1.5 Ah) is integrated with a photovoltaic panel to demonstrate the practical renewable energy storage capabilities. Our superior anode-free Zn batteries enabled by the heterostructured interface enlighten an arena towards large-scale energy storage applications. The development of dendrite-free, Zn-free anodes is challenging. Here, the authors design a two-dimensional antimony/antimony-zinc alloy heterostructured interface to achieve dendrite-free Zn deposition with areal capacity of 200 mAh cm −2 , and energy density of around 270 Wh kg −1 for anode-free zinc-bromine battery.

The acceleration of energy exhaustion and environmental pollution calls for the development of electrocatalytic conversion and storage technologies for the production and utilization of green energy. However, these technologies have encountered significant challenges in terms of poor selectivity, high overpotential, low efficiency, and sluggish kinetics of the electrocatalytic reactions, which involve solid–liquid–gas three‐phase interfaces (SLG‐TPIs). In this review, we focus on discussing recent progress on the development of SLG‐TPIs for electrocatalytic reactions, such as hydrogen evolution reaction (HER), oxygen evolution and reduction reactions (OER/ORR), and carbon dioxide reduction reaction (CO2RR), as well as their applications in water splitting, fuel cells, and metal‐air batteries. In addition, the working mechanism of TPIs is described and revealed by advanced characterization tools. The challenges and future opportunities of TPIs for electrocatalysis are also proposed in this review. This review focuses on discussing recent progress on the development of solid–liquid–gas three‐phase interfaces for electrocatalytic reactions of hydrogen evolution, oxygen evolution and reduction, and carbon dioxide reduction with applications in water splitting, fuel cells and metal‐air batteries.

The utilization of polyethylene terephthalate (PET) powder as aggregate in the development of environmentally friendly high-ductility composites (P-EHDC) offers a promising pathway for advancing sustainable and high-performance concrete materials. Despite its potential, the fracture behavior of P-EHDC—particularly under the influence of alkali-activated precursors—remains insufficiently explored. In this study, the fracture performance of P-EHDC was evaluated by varying the precursor composition ratios (GGBS:FA = 4:6, 3:7, and 2:8) and PET powder replacement ratios (0%, 15%, 30%, and 45% by volume). Fracture modes, Mode I fracture energy (GF), and crack propagation behavior were analyzed using the J-integral method. All specimens exhibited ductile fracture characteristics, a clear contrast to the brittle failure observed in conventional concrete. The replacement of 15 vol% PET powder significantly increased GF in precursor systems with higher GGBS content (4:6 and 3:7), and 30 vol% was more effective in fly ash-rich systems (2:8). The J-integral method, which offers broader applicability compared to conventional methods such as the double-K fracture model, provided a more comprehensive understanding of the fracture behavior. The results showed that PET powder reduced the matrix fracture toughness, promoted matrix cracking, and weakened the fiber-bridging effect, leading to enhanced energy absorption via fiber pull-out. At low PET powder replacement ratios (e.g., 15 vol%), the cracking threshold of the matrix was not significantly reduced, while more fibers engaged during the crack instability stage to absorb fracture energy through pull-out. This behavior highlights the synergistic toughening effect between PET powder and fibers in the P-EHDC system. The effect became more pronounced when the PET content was below 45 vol% and the precursor matrix contained a higher proportion of GGBS, leading to enhanced ductility. This study introduces a novel approach to fracture behavior analysis in PET-modified alkali-activated composites and provides theoretical support for the toughening design of high-performance, low-carbon concrete materials.

Low carbon and high performance have become key trends in the development of construction materials. Understanding the mechanism by which curing conditions affect the mechanical properties of high-ductility geopolymer concrete (HDGC) is of significant importance. This study investigated three sealing curing temperatures (room temperature, 45 °C, and 60 °C) and four curing durations (1 day, 3 days, 5 days, and 7 days), while considering two final curing ages (7 days and 28 days) to explore their effects on the axial tensile and compressive properties of HDGC. The results showed that both 45 °C and 60 °C could improve the brittle failure of HDGC under axial compressive loading. However, curing at 60 °C and for durations longer than 1 day in an oven would catalyze the formation of eight-faced zeolite crystals within the slag–fly ash geopolymer matrix, and it could weaken the matrix’s pore structure and subsequently affect the material’s later strength development. Nevertheless, oven heat curing enhanced the bridging effect between the fibers and the matrix, partially compensating for the reduction in the initial tensile strength of HDGC. This follows the pseudo-strain-hardening material’s saturation cracking criterion to enhance the strain-hardening behavior of HDGC and improve its tensile energy absorption capacity. A curing condition of 45 °C for 5 days is recommended to maximize the ductility of HDGC. This study provides important theoretical support for the design and promotion of green, low-carbon, high-ductility composite materials.

Neuroinflammation has been proven to exert an important effect on brain injury after intracerebral hemorrhage (ICH). Previous studies reported that Didymin possessed anti-inflammatory properties after acute hepatic injury, hyperglycemia-induced endothelial dysfunction, and death. However, the role of Didymin in microglial pyroptosis and neuroinflammation after ICH is unclear. The current study aimed to investigate the effect of Didymin on neuroinflammation mediated by microglial pyroptosis in mouse models of ICH and shed some light on the underlying mechanisms. In this study, we observed that Didymin treatment remarkably improved neurobehavioral performance and decreased BBB disruption and brain water content. Microglial activation and neutrophil infiltration in the peri-hematoma tissue after ICH were strikingly mitigated by Didymin as well. At the molecular level, administration of Didymin significantly unregulated the expression of Rkip and downregulated the expression of pyroptotic molecules and inflammatory cytokines such as Nlrp3 inflammasome, GSDMD, caspase-1, and mature IL-1β, TNF-α, and MPO after ICH. Besides, Didymin treatment decreased the number of Caspase-1-positive microglia and GSDMD-positive microglia after ICH. Inversely, Locostatin, an Rkip-specific inhibitor, significantly abolished the anti-pyroptosis and anti-neuroinflammation effects of Didymin. Moreover, Rkip binding with Asc could interrupt the activation and assembly of the inflammasome. Mechanistically, inhibition of Caspase-1 by VX-765 attenuated brain injury and suppressed microglial pyroptosis and neuroinflammation by downregulation of GSDMD, mature IL-1β, TNF-α, and MPO based on Locostatin-treated ICH. Taken together, Didymin alleviated microglial pyroptosis and neuroinflammation, at least in part through the Asc/Caspase-1/GSDMD pathway via upregulating Rkip expression after ICH. Therefore, Didymin may be a potential agent to attenuate neuroinflammation via its anti-pyroptosis effect after ICH.

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target.

Intracerebral hemorrhage (ICH) is a major cerebrovascular illness that causes substantial neurological sequelae and dysfunction caused by secondary brain injury (SBI), and there are no effective therapies to mitigate the disability. Microglia, the brain-resident macrophage, participates in the primary inflammatory response, and activation of microglia to an M1-like phenotype largely takes place in the acute phase following ICH. A growing body of research suggests that the pathophysiology of SBI after ICH is mediated by an inflammatory response mediated by microglial-pyroptotic inflammasomes, while inhibiting the activation of microglial pyroptosis could suppress the inflammatory cascade reaction, thus attenuating the brain injury after ICH. Pyroptosis is characterized by rapid plasma membrane disruption, followed by the release of cellular contents and pro-inflammatory mediators. In this review, we outline the molecular mechanism of microglial pyroptosis and summarize the up-to-date evidence of its involvement in the pathological process of ICH, and highlight microglial pyroptosis-targeted strategies that have the potential to cure intracerebral hemorrhage. This review contributes to a better understanding of the function of microglial pyroptosis in ICH and assesses it as a possible therapeutic target.

Post-stroke spasticity seriously affects patients’ quality of life. Spasticity is considered to involve both neural and non-neural factors. Current clinical scales, such as the Modified Ashworth Scale and the Modified Tardieu Scale, lack reliability and reproducibility. These scales are also unable to identify the neural and non-neural contributions to spasticity. Surface electromyography and biomechanical and myotonometry measurement methods for post-stroke spasticity are discussed in this report. Surface electromyography can provide neural information, while myotonometry can estimate muscular properties. Both the neural and non-neural contributions can be estimated by biomechanical measurement. These laboratory methods can quantitatively assess spasticity. They can provide more valuable information for further study on treatment and rehabilitation than clinical scales.