Design and Implementation of Electrochromic Smart Windows with Self-Driven Thermoelectric Power Generation.

Electrochromic smart windows can achieve controllable modulation of color and transmittance under an external electric field with active light and thermal control capabilities, which helps reduce energy consumption caused by building cooling and heating. However, electrochromic smart windows often rely on external power circuits, which greatly affects the independence and portability of smart windows. Based on this, an electrochromic smart window driven by temperature-difference power generation was designed and implemented. This smart window provides automatic and manual control of the reversible cycle of electrochromic glass from light blue to dark blue according to user requirements and changes in the surrounding environment, achieving adaptive adjustment of visual comfort and reducing energy consumption. The infrared radiation rejection (from 780 to 2500 nm) of the electrochromic smart window is as high as 77.3%, and its transmittance (from 380 to 780 nm) fluctuates between 39.2% and 56.4% with changes in working state. Furthermore, the temperature in the indoor simulation device with electrochromic glass as the window was 15 °C lower than that with ordinary glass as the window after heating with a 250 W Philips infrared lamp for ten minutes. After 2000 cycles of testing, the performance of the smart window was basically maintained at its initial values, and it has broad application prospects in buildings, vehicles, and high-speed rail systems.

Passive white light cavities with metal coatings.

White light cavities with broadband resonance are usually filled with negative dispersion medium, which inevitably leads to gain. In this article, pure passive white light cavities are designed, in which negative dispersion medium is no longer necessary. Theoretically, if the reflection phase of the cavity wall can exhibit a negative dispersion slope, then it can also satisfy white light cavities conditions without medium. In practice, the negative dispersion property of the cavity wall can be realized by two metal coatings with different reflection coefficients. Therefore, our white light cavities are composite cavities, in which the main cavity provides resonance while the auxiliary cavity forms the cavity wall, providing negative dispersion reflection phase. Further, atomic gas can be employed to improve the performance of the white light cavities. Atomic gas exploits effects such as Electromagnetic Induced Transparency (EIT), enabling the white light cavities to be controlled by coherent driving field. With the passive characters, our design can be realized and implemented much more easily.

An adsorption isotherm identification method based on CNN-LSTM neural network.

CONTEXT: The morphology of adsorption isotherms embodies a wealth of information regarding various adsorption mechanisms, rendering the classification and identification methodologies predicated on the shape of adsorption isotherms indispensably crucial. While research on classification techniques has been extensively developed, traditional methods of adsorption isotherm identification grapple with inefficiencies and a high margin of error. Neural network-based methodologies for adsorption isotherm identification serve as a countermeasure to these shortcomings, as they facilitate swift online identification while delivering precise results. In this paper, we deploy a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) networks for the identification of adsorption isotherms. Extensive theoretical adsorption isotherms are generated via adsorption equations, forming a comprehensive training database, thereby circumventing the need for time-consuming and costly repetitive experiments. The F1-score, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) are introduced as criteria to evaluate the identification performance and generalization ability of the model during the testing phase. The results highlight the model's superlative performance in the task of adsorption isotherm identification, with accuracy rates of 100% in both the training and validation sets. The mean F1-score obtained from the testing set reached 0.8885, with both macro-average and micro-average AUC exceeding 0.95. METHOD: PyCharm was employed as an experimental and testing platform, with Python 3.9 serving as the programming language. TensorFlow 2.11.0 and Keras 2.10.0 were harnessed for the training and testing of CNN-LSTM, while numpy 1.21.5 and scipy 1.81 were utilized for the creation of training and validation datasets.

Phosphorus-doped helical carbon nanofibers as enhanced sensing platform for electrochemical detection of carbendazim.

A combined chemical vapor deposition with high-pressure annealing has been developed for the production of phosphorus-doped helical carbon nanofibers (P-HCNFs). The resulting P-HCNFs have a large specific surface area, well-defined three-dimensional hierarchical helical structure and rapid apparent heterogeneous electron transfer. Based on the high electrocatalytic activity, the P-HCNFs were used to develop an amperometric sensor for carbendazim detection. The experimental results demonstrated that the sensor is promising for the determination of carbendazim in food samples due to the high sensitivity, wide linear range and low detection limit.