MAlSiN₃:Eu²⁺ (M = Ca, Sr) is commonly used in high-power phosphor-converted white-light-emitting diodes and laser diodes to promote their color-rendering index. However, the wide application of this phosphor is limited by the degradation of its luminescent properties in high-temperature, high-humidity, and high-sulfur-content environment. Here, the degradation mechanism of the (Sr,Ca)AlSiN₃:Eu²⁺ (SCASN) red phosphor under thermal-moisture-sulfur coupling conditions is investigated. Furthermore, by performing first-principles calculations, the hydrolysis mechanism on an atomic scale is assessed. The adsorption energy (E_ads) and charge transfer (ΔQ) results showed that H₂O chemically adsorbed on the (0 1 0), (3 1 0), and (0 0 1) surfaces of the CaAlSiN₃ (CASN) host lattice. The energy barrier for H₂O dissociation is only 29.73 kJ mol⁻¹ on the CASN (0 1 0) surface, indicating a high dissociation probability. The formation of NH₃, Ca(OH)₂, and CaAl₂Si₂O₈ is confirmed by H⁺ tended to combine with surface N atoms, while OH⁻ combined with the surface Al/Si or Ca atoms. Moreover, ab initio molecular dynamics simulations were performed to further understand the hydrolysis process. This work offers a guidance on the design and applications of luminescent materials in LED packages with higher reliability and stability requirements in harsh environment.

High-power white light-emitting diodes (LEDs) have demonstrated superior efficiency and reliability compared to traditional white light sources. However, ensuring maximum performance for a prolonged lifetime use presents a significant challenge for manufacturers and end users, especially in safety–critical applications. Thus, identifying functional anomalies and predicting the remaining useful lifetime (RUL) is of enormous importance in the operational longevity of the device. To address such challenges, this study proposes a combination of distance-based Mahalanobis distance (MD), entropy generation rate (EGR), and deep learning models for improved anomaly detection and RUL prediction accuracy. Unlike conventional health indicators based on luminous flux data that are challenging to monitor relevant optical performance, the MD and EGR methods are employed to extract in-situ monitored thermal and electrical data as new health indicators. Long short-term memory recurrent neural networks (LSTM-RNN) and convolutional neural networks (CNN) are established to detect anomalies and predict the RUL. The accelerated degradation tests of 3 W high-power white LED have been conducted, and the online and offline collected experimental data are deployed for model development and performance evaluation. The performance of the proposed methods is compared against the Illuminating Engineering Society of North America (IESNA) TM-21 method. The results indicate that LSTM-RNN, when combined with either MD or EGR, can detect anomalies with significantly fewer data (70 %) than is typically required. Furthermore, a significant improvement in prediction accuracy in RUL prediction based on MD and EGR-constructed time series health indicators and employed with the LSTM-RNN model demonstrates the effectiveness of the proposed methods.