Jin Wang

Evaporating sessile droplets are usually treated as capillary objects, but droplets generated by routine handling can carry tens to hundreds of picocoulombs of electric charge. Here, we combine Faraday-cup charge measurements with optical imaging to determine how such charge evolves as water droplets evaporate on dry polymer substrates. A zero-time protocol shows that a reproducible initial charge is preserved on poly(methylpentene) (PMP), whereas polydimethylsiloxane, slippery omniphobic covalently attached liquid surfaces, and polystyrene yield substrate-dependent recovered-charge responses, including heterogeneous exchange, rapid net charge relaxation, and interfacial charging. On PMP, ensemble-resolved measurements reveal two regimes: the charge remains nearly constant during early evaporation and then decreases abruptly once the droplet reaches a small-volume state. This charge collapse coincides with spontaneous lateral translation rather than jetting or breakup. A Rayleigh-normalized analysis, including a spherical-cap stress correction and an effective contact-line retention scale, shows that motion occurs only after evaporation drives the droplet into a high electro-pinning state. High-speed imaging and kinematic analysis support a picture in which the subsequent motion is governed by repeated contact-line depinning and re-pinning: the total distance traveled is strongly affected by dry-surface pinning, whereas the peak translational velocity serves as a more robust indicator of the discharge strength. Footprint-shape statistics further show that visible pre-depinning elongation relaxes as translation accelerates but that its major axis does not predict the rapid-burst direction. Complementary side-view measurements reveal a transient left–right apparent-angle asymmetry during slip. These results identify a dry-substrate mode of evaporation-driven electrostatic relaxation, distinct from Coulomb fission on lubricated surfaces, in which substrate electrostatic passivity enables charge retention, droplet geometry selects the instability onset, and whole-droplet translation provides the charge-release pathway.

Accurate monitoring of temperature, axial strain, and refractive index is essential for structural health monitoring, industrial process control, and environmental sensing. However, conventional optical fiber sensors often suffer from strong parameter cross-sensitivity and limited discrimination capability when multiple parameters must be measured simultaneously. In this work, a compact multi-parameter optical fiber sensing platform based on a cascaded single-mode fiber (SMF), multimode fiber (MMF), and long-period fiber grating (LPFG) structure is proposed and combined with a wavelength-based spectral demodulation strategy. Within this cascaded configuration, multiple characteristic spectral troughs originating from different physical mechanisms coexist in a single transmission spectrum, including multimode interference in the MMF section and resonance coupling induced by the LPFG. Instead of assigning individual sensing elements to different parameters, the proposed approach exploits the distinct and linearly independent responses of multiple spectral troughs to temperature, axial strain, and refractive index. Each trough is therefore represented by a sensitivity vector, forming a multi-trough sensitivity framework that enables robust multi-parameter discrimination through matrix-based spectral demodulation. Experimental results demonstrate highly linear and repeatable wavelength shifts of three representative troughs under variations of temperature, strain, and refractive index. The measured temperature sensitivities are −0.0733, −0.0833, and −0.0733 nm/°C, while the corresponding strain sensitivities reach −0.6706, −0.6916, and −0.8026 nm/mε, all with coefficients of determination exceeding 0.99. Under refractive index modulation, two troughs exhibit negligible responses below 0.0006 nm/%, whereas the third shows a pronounced sensitivity of −0.1426 nm/% with excellent linearity. By exploiting the differential sensitivity characteristics of multiple spectral troughs within a single cascaded fiber structure, effective discrimination of temperature, axial strain, and refractive index is achieved without additional sensing elements or complex optical paths. Long-term stability tests further confirm wavelength fluctuations within ±0.01 nm, demonstrating the excellent stability and repeatability of the proposed sensing platform.

Real-time and decoupled measurement of multiple environmental parameters remains a key challenge in optical fiber sensing due to cross-sensitivity and system complexity. In this work, a cascaded polarization maintaining fiber (PMF) Sagnac interferometer is proposed for simultaneous measurement of humidity, pH, and temperature. The sensing system is implemented within a single Sagnac loop using cascaded PMFs, forming a homogeneous and integrated architecture. The PMF cladding is functionalized with a microporous polyvinyl alcohol/CNs composite film for humidity sensing and a poly(allylamine hydrochloride)/poly(acrylic acid) multilayer nanofilm for pH sensing, while an uncoated PMF segment serves as a temperature reference. By combining the vernier effect with a multi-wavelength matrix demodulation method, crosstalk-free decoupling of the three parameters is achieved without requiring multiple discrete sensors or complex demodulation schemes. The proposed sensor exhibits a humidity sensitivity of 0.26 nm/%RH, a pH sensitivity of 1.03 nm/pH, and a temperature sensitivity of 1.45 nm °C ⁻¹ . The system also demonstrates good repeatability, low hysteresis (<1%), and stable performance with minimal drift under constant conditions. Compared with conventional multi-parameter sensing approaches based on hybrid fiber structures or multi-device integration, the proposed method provides a simplified configuration, reduced demodulation complexity, and improved scalability. This work offers a practical solution for multi-parameter monitoring in coupled environments such as confined or closed systems where humidity and acidity vary simultaneously.

Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain limited. Therefore, we evaluate 11 conventional ML models alongside the graph neural network-based Crystal Graph Convolutional Neural Network (CGCNN) for predicting three key properties—formation energy (E_f), band gap (E_g), and energy above hull (E_h)—across a dataset comprising single perovskites, double perovskites, and their combined structures. The results demonstrate that for single perovskites, CGCNN exhibits gains of over 20% in the root mean square error (RMSE) relative to the second-best model (Gradient Boosting Regression), achieving values of 0.205 eV/atom (E_f), 0.718 eV (E_g), and 0.167 eV/atom (E_h). Prediction accuracy for double perovskites is significantly enhanced by training CGCNN on a combined dataset, particularly for E_h, where the coefficient of determination (R²) improves approximately 68.1-fold compared to models trained exclusively on double-perovskite data. Feature importance analysis via one-shot, permutation-based, and recursive feature elimination (RFE) methods reveals that optimal model performance requires retention of at least the top 20 critical features. Furthermore, feature utilization patterns of CGCNN across different prediction tasks are visualized. This work provides actionable guidelines for model selection and feature engineering in perovskite property prediction, establishing a benchmark for future ML-driven materials discovery.

Accurate monitoring of temperature, axial strain, and refractive index is critical for structural health monitoring, industrial process control, and environmental sensing. However, conventional optical fiber sensors are often limited by strong parameter cross sensitivity, poor discrimination capability, and increased system complexity when multiple sensing units are required. In this work, a compact multi-parameter optical fiber sensing platform is proposed based on a cascaded single-mode fiber, multimode fiber, and long-period fiber grating structure, combined with a wavelength-based spectral demodulation strategy. Within the cascaded configuration, multiple characteristic spectral troughs arising from distinct physical mechanisms coexist in a single transmission spectrum. Interference-induced troughs are generated by the multimode fiber section, while a resonance-induced trough is introduced by the long-period fiber grating. Although none of these troughs responds exclusively to a single parameter, each exhibits simultaneous and linearly independent responses to temperature, axial strain, and refractive index with distinct sensitivity magnitudes and trends. Consequently, each trough can be described by a unique sensitivity vector, enabling robust multi-parameter discrimination through multi-wavelength spectral demodulation.

Although perovskite has great potential in optoelectronic devices, the simultaneous satisfaction of material stability and high performance is still an issue that needs to be solved. Most perovskite optoelectronic devices use quantum dot spin coating or the gas-phase growth of perovskite thin films as the photoelectric conversion layer. Due to stability limitations, these materials often experience a significant decrease in photoelectric conversion efficiency when encountering liquid reagents. The self-assembled growth of hybrid perovskite crystals determines superior lattice ordering and stability. There are three types of ionic liquids—[Emim]BF4, EMIMNTF2, and HMITFSI—that can effectively enhance the X-ray photoelectric conversion performance of hybrid perovskite crystal CH₃NH₃PbI₃ (MAPbI₃), and the enhancement in the photocurrent leads to an improvement in the sensitivity of X-ray detectors. We soak the perovskite crystals in an ionic liquid and perform two treatment methods: electrification and dilution with ETOH solution. It is interesting to find that MAPbI₃ perovskite single crystal materials choose the same optimized ionic liquid species in X-ray detection and photovoltaic power generation applications, and the effect is quite the opposite. Compared with untreated MAPbI₃ crystals, the average photocurrent density of Electrify-HMITFSI MAPbI₃ increased by 826.85% under X-ray excitation and the sensitivity of X-ray detectors made from these treated MAPbI₃ crystals significantly increased by 72.6%, but the intensity of the PL spectrum decreased to 90% of the untreated intensity.