Chiral organic-inorganic hybrids combining chirality of organic molecules and semiconducting properties of inorganic frameworks generate chiral excitons without external spin injection, creating the potential for chiroptoelectronics. However, the relationship between molecular chirality and exciton chirality is still unclear. Here we show the strain-amplified exciton chirality in one-dimensional chiral metal halides. Utilizing chirality-induced spin-orbital coupling theory, we quantitatively demonstrate the impact of the strain-engineered molecular assembly of chiral cations on exciton chirality, offering a feasible way to amplify exciton chirality by molecular manipulation.

Through first-principle calculations, the electronic properties of the PtSe₂/Hf₂CO₂ van der Waals heterostructure can be modulated by electric fields and biaxial strain, showing its potential applications in various devices.

To achieve high-quality voice communication technology without noise interference in flammable, explosive and strong electromagnetic environments, the speech enhancement technology of a fiber-optic external Fabry–Perot interferometric (EFPI) acoustic sensor based on deep learning is studied in this paper. The combination of a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) model is proposed for speech enhancement in the EFPI acoustic sensing system. Moreover, the 3 × 3 coupler algorithm is used to demodulate voice signals. Then, the short-time Fourier transform (STFT) spectrogram features of voice signals are divided into a training set and a test set. The training set is input into the established CV-CNN-LSTM model for model training, and the test set is input into the trained model for testing. The experimental findings reveal that the proposed CV-CNN-LSTM model demonstrates exceptional speech enhancement performance, boasting an average Perceptual Evaluation of Speech Quality (PESQ) score of 3.148. In comparison to the CV-CNN and CV-LSTM models, this innovative model achieves a remarkable PESQ score improvement of 9.7% and 11.4%, respectively. Furthermore, the average Short-Time Objective Intelligibility (STOI) score witnesses significant enhancements of 4.04 and 2.83 when contrasted with the CV-CNN and CV-LSTM models, respectively.