The increasing number of spent lithium-ion batteries demands the development of efficient and environmentally friendly recycling technologies. This study explores the carbothermal reduction (CR) of LiCoO2-graphite mixtures with mass ratios of 32 & ratio;1 (Group A) and 10 & ratio;1 (Group B). Thermal analysis and kinetic models (KAS and FWO) revealed that higher heating rates shifted pyrolysis peaks to higher temperatures. Group B exhibited the greater weight loss and a higher activation energy (KAS: 240.64 kJ/mol; FWO: 234.08 kJ/mol) than Group A. Product analysis indicated that at 800 degrees C, Group A produced Li2CO3 and CoO, while Group B yielded Li2CO3 and metallic Co. By adjusting the mixing ratio and temperature, lithium and cobalt can be selectively recovered, providing a theoretical basis for sustainable battery recycling.
Balancing high dynamic response and high thrust density remains a prominent challenge for advanced permanent magnet linear motors (PMLMs), which has aroused persistent research interest. In contrast to conventional parametric iteration approaches relying merely on finite element analysis (FEA), this work develops an analytical‑guided parametric design framework for cylindrical Halbach moving-coil linear actuators (MCHLAs) to improve thrust performance.An analytical field model is constructed by solving the Laplace equation via separation of variables and Fourier series, which quantitatively establishes the parametric-thrust correlation and overcomes the drawback that pure FEA fails to reveal implicit parameter coupling mechanisms. Using the derived analytical model, the influences of pole-arc-to-pole-pitch ratio and radial dimensions on electromagnetic output are elaborated, and three-dimensional FEA is subsequently applied to acquire the geometric configuration.Prototype experiments validate the effectiveness of the design obtained from the analytical‑guided parametric design framework, the peak radial and axial air-gap flux densities of the MCHLA reach 1.2 T and 1.05 T, alongside an average thrust of 359.1 N. Dynamic tests confirm superior transient performance: the mover accelerates from rest to 0.98 m/s within 10 ms under no load, and an initial 27.41 mm impact vibration is rapidly eliminated within 400 ms with stable thrust and back-EMF waveforms. The proposed design strategy can facilitate the development of PMLMs for magnetic levitation and active vibration control applications.
To improve athletic performance and reduce risk of overtraining or injury, managing and optimizing training load will be critical to achieving this goal. Both traditional methods, which involved manual tracking and feedback, and existing cloud solutions that introduce latency and inadequate adaptive mechanisms to manage the noise of multimodal IoT sensor-generated data, limit the ability to make informed, rapid decisions. The purpose of this research is to develop a framework that predicts and optimizes traditional training load in real-time at the edge by using advanced deep-learning techniques in combination with IoT sensor data. The development of a Self-adaptive Long Short-Term Memory (SA-LSTM) network enhances dynamic temporal modeling and hyperparameter tuning with the addition of the Fossa Optimization Algorithm (FOA). This new framework collects multimodal Internet of Things (IoT) sports data including heart rate, acceleration, speed and movement. Preprocessing involves Savitzky-Golay filter, which removes noise, and Min-Max normalization, which scales features to the same range. Power Spectral Density (PSD) in the frequency domain is used to capture periodicity and energy density, both of which are required for assessing training loads. The attention mechanism enables the algorithm to prioritize critical time points for immediate prediction. Implementation is completed using Python, with TensorFlow/Keras, NumPy and Pandas, optimised for deployment to the edge. The proposed framework obtained an accuracy of 98.7
The control of noise pollution in the low-frequency domain with various spectrum ranges requires the development of a novel tunable sound absorbing material, and a multilayer stackable grouped acoustic metamaterial (MSG-AM) is proposed in this research to obtain the optimal noise reduction effect while minimizing the occupied space. The MSG-AM includes four layers with the eight Helmholtz resonators in each layer divided into three groups, and the total 32 resonators in different layers are connected in parallel. The sound absorption property of each layer is optimized by the joint simulation of finite element simulation and particle swarm optimization algorithm, and the average sound absorption coefficient (SAC) reaches 0.8609 for the first layer in the 535–650 Hz, 0.7126 for the second layer in the 782–937 Hz, 0.9285 for the third layer in the 650–782 Hz, and 0.7615 for the fourth layer in the 435–535 Hz, respectively. The four layers can be combined flexibly to gain the desired sound absorption property in the expected frequency range, and the sequence of multiple layers is a critical factor. The average SAC of the MSG-AM with four layers is improved from 0.3696 for 4 + 1 + 3 + 2 to 0.8726 for 2 + 3 + 1 + 4, and it is better to put behind the layer to obtain absorption in the low-frequency domain and put in front the layer to achieve absorption in the high-frequency domain. The sound absorption mechanism is revealed intuitively through the distributions of thermal power densities, which certify that there exists Helmholtz resonance and Fabry–Pérot resonance effects simultaneously. The proposed MSG-AM is conducive to guarantee the effective control of variable noise.
The 1,2,4-trichlorobenzene (1,2,4-TCB) is a persistent organic pollutant, which poses a serious concern due to its long-lasting and detrimental impact on soil and water quality. This study uses meta-analysis to investigate the effectiveness of various remediation methods for 1,2,4-TCB in water and soil. In water, the intimate coupling of photocatalysis and biodegradation (ICPB) demonstrated the highest removal rate (80
