Data-Driven Photoluminescence Tuning in Eu2+-Doped Phosphors

High-Performance Tunable Near-Infrared Emitters of Cr3+-Activated Garnet Phosphor Enabled by Chemical Unit Co-Substitution

The escalating demand for portable near-infrared (NIR) light sources has posed a formidable challenge to the development of NIR phosphors characterized by high efficiency and exceptional thermal stability. Taking inspiration from the chemical unit co-substitution strategy, high-performance tunable (Lu3- xCax)(Ga5- xGex)O12:6%Cr3+ (x = 0-3) phosphors are designed with an emission center from 704 to 780 nm and a broadest full width at half maximum (FWHM) of up to 172 nm by introducing Ca2+ and Ge4+ ions into the garnet structure. In particular, Lu3Ga5O12:6%Cr3+ demonstrates an anti-thermal quenching phenomenon (I423K = 113.1%). Compared to Lu3Ga5O12:6%Cr3+, Lu2CaGa4GeO12:6%Cr3+ exhibits significantly improved FWHM and IQE by 108 nm and 25.5%, respectively, while maintaining good thermal stability (I423K = 80.4%). Finally, Lu2CaGa4GeO12:6%Cr3+ phosphor is combined with a 465 nm blue LED chip to fabricate NIR LED devices, exhibiting a NIR electroluminescence efficiency of 13.31%@100 mA and demonstrating successful applications in nocturnal illumination and biomedical imaging technology. This work offers a fresh perspective on the design of highly efficient NIR garnet phosphors.

Broadband sensitized near-infrared emission in Eu2+-Nd3+ co-doped BaAl2O4 as a potential spectral modulator for silicon solar cells

The near-infrared (NIR) down-conversion process for broadband sensitization has been studied in Eu2+-Nd3+ co-doped BaAl2O4. This material has a broad absorption band of 200-480 nm and can convert photons in the visible region into NIR photons. The NIR emission at 1064 nm, attributed to the Nd3+:4F3/2 → 4I11/2 transition, matches the bandgap of Si, allowing Si solar cells to utilize the solar spectrum better. The energy transfer (ET) process between Eu2+ and Nd3+ was demonstrated using photoluminescence spectra and luminescence decay curves, and Eu2+ may transfer energy to Nd3+ through the cooperative energy transfer (CET) to achieve the down-conversion process. The energy transfer efficiency (ETE) and theoretical quantum efficiency (QE) were 68.61% and 156.34%, respectively, when 4 mol% Nd3+ was introduced. The results indicate that BaAl2O4:Eu2+-Nd3+ can serve as a potential modulator of the solar spectrum and is expected to be applied to Si solar cells.

GPT-Assisted Learning of Structure-Property Relationships by Graph Neural Networks: Application to Rare-Earth-Doped Phosphors

Two challenges facing machine learning tasks in materials science are data set construction and descriptor design. Graph neural networks circumvent the need for empirical descriptors by encoding geometric information in graphs. Large language models have shown promise for database construction via text extraction. Here, we apply OpenAI's Generative Pre-trained Transformer 4 (GPT-4) and the Crystal Graph Convolutional Neural Network (CGCNN) to the problem of discovering rare-earth-doped phosphors for solid-state lighting. We used GPT-4 to datamine the chemical formulas and emission wavelengths of 264 Eu2+-doped phosphors from 274 articles. A CGCNN model was trained on the acquired data set, achieving a test R2 of 0.77. Using this model, we predicted the emission wavelengths of over 40 000 inorganic materials. We also used transfer learning to fine-tune a bandgap-predicting CGCNN model for emission wavelength prediction. The workflow requires minimal human supervision and is generalizable to other fields.

Isolated Coordination Polyhedron Confinement in ABP2O7:Mn2+ (A = Ba/Sr; B = Mg/Zn)

Many research efforts have focused on designing new inorganic phosphors to meet different application requirements. The structure-photoluminescence relationship between activator ions and the matrix lattice plays an irreparable role in designing target phosphors. Herein, a series of ABP2O7:Mn2+ (A = Ba/Sr; B = Mg/Zn) phosphors are prepared for a detailed study on the relationship between the luminescence performance and spatial structure and symmetry of the doping site of Mn2+. Due to the weak interaction between nearest B-B pairs, [BO5] is defined as an isolated coordination polyhedron whose structure and symmetry directly influence the photoluminescence of Mn2+. The emission wavelength of Mn2+ is ∼620 nm when it occupies the triangular bipyramid [MgO5] in BaMgP2O7. When Mn2+ occupies the quadrangular pyramid-typed [MgO5] or [ZnO5] in SrMgP2O7, SrZnP2O7, and BaZnP2O7, the emission wavelengths peak at ∼670 nm. We propose a conception of isolated coordination polyhedral confinement to clarify the luminescence performance of Mn2+ in the fivefold coordination configuration with different geometries, which has great theoretical research significance for designing inorganic phosphors.

Unleashing the Power of Artificial Intelligence in Materials Design

The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.

Recent Advances in Light-Conversion Phosphors for Plant Growth and Strategies for the Modulation of Photoluminescence Properties

The advent of greenhouses greatly promoted the development of modern agriculture, which freed plants from regional and seasonal constraints. In plant growth, light plays a key role in plant photosynthesis. The photosynthesis of plants can selectively absorb light, and different light wavelengths result in different plant growth reactions. Currently, light-conversion films and plant-growth LEDs have become two effective ways to improve the efficiency of plant photosynthesis, among which phosphors are the most critical materials. This review begins with a brief introduction of the effects of light on plant growth and the various techniques for promoting plant growth. Next, we review the up-to-date development of phosphors for plant growth and discussed the luminescence centers commonly used in blue, red and far-red phosphors, as well as their photophysical properties. Then, we summarize the advantages of red and blue composite phosphors and their designing strategies. Finally, we describe several strategies for regulating the spectral position of phosphors, broadening the emission spectrum, and improving quantum efficiency and thermal stability. This review may offer a good reference for researchers improving phosphors to become more suitable for plant growth.

Ion Substitution Strategy toward High-Efficiency Near-Infrared Photoluminescence of Cs2KIn1-yAlyF6:Cr3+ Solid Solutions

The rising demand for portable near-infrared (NIR) light sources has accelerated the exploration of NIR luminescent materials with high efficiency and excellent thermal stability. Inspired by the structural-modulated ion substitution strategy, herein, a high-performance Cs₂KIn_0.8Al_0.1F₆:0.1Cr³⁺ phosphor with a peak at 794 nm and full width at half-maximum (fwhm) of 117 nm was successfully synthesized by introducing Al³⁺ ions. The high performance is reflected in its high internal quantum efficiency (IQE) of 88.06% and good thermal quenching resistance (I_423K = 71.64%). Compared with the initial Cs₂KInF₆:0.1Cr³⁺, the IQE and thermal stability are improved by 16.67% and 72.54%, which stem from the enhanced crystallinity and the strengthened structural rigidity. Finally, a phosphor-converted light-emitting diode (pc-LED) with a superior NIR photoelectric efficiency (21.04%@320 mA) was fabricated. Meanwhile, the pupil tracking, anticounterfeiting, intelligent identification, and bioimaging were successfully demonstrated. This work provides new perspectives for synthesizing efficient NIR fluoride phosphors and designing diverse applications.

Tailoring the Luminescent Properties of SrS:Ce3+ by Sr-Deficiency and Na+ Doping

Ce³⁺-doped SrS phosphors with a charge-compensating Na⁺ addition were successfully synthesized via a solid-state reaction method, and the related X-ray diffraction patterns can be indexed to the rock-salt-like crystal structure of the Fm3̅m space group. SrS:(Ce³⁺)_x (0.005 ≤ x ≤ 0.05) and SrS:(Ce³⁺)_0.01,(Na⁺)_y (0.005 ≤ y ≤ 0.030) phosphors were excited by 430 nm UV-Vis light, targeted to the 5d¹ → 4f¹ transition of Ce³⁺. The composition-optimized SrS:(Ce³⁺)_0.01, (Na⁺)_0.015 phosphors showed an intense broad emission band at λ = 430-700 nm. The doping of Na⁺ was probed by solid-state nuclear magnetic resonance. The 430 nm pumped white light-emitting diode structure fabricated with a combination of SrS:(Ce³⁺)_0.01,(Na⁺)_0.015 and Sr₂Si₅N₈:Eu²⁺ phosphors shows a color-rendering index (R_a) of 89.7. The proposed strategy provides new avenues for the design and realization of novel high color quality solid-state LEDs.

A novel differential display material: K3LuSi2O7: Tb3+/Bi3+ phosphor with thermal response, time resolution and luminescence color for optical anti-counterfeiting

Optical anti-counterfeiting and encryption have become a hotspot in information security. However, the advanced optical anti-counterfeiting technology still suffers from low security by single-luminescent mode. Herein, we present a novel multi-mode anti-counterfeiting strategy based on K₃LuSi₂O₇: Tb³⁺/Bi³⁺ (KLSO: Tb³⁺/Bi³⁺) phosphors for the first time. KLSO not only provides various lattice sites for Bi³⁺ ions occupying to achieve tunable luminescence but can also be non-equivalently substituted by Tb³⁺ ions to produce persistent or thermo-luminescence. Furthermore, in the pattern "8888" constructed by the mixture of polyacrylic acid (PAA) with KLSO: Tb³⁺/Bi³⁺ phosphors, we selectively trigger the three luminescent modes of Bi³⁺ and Tb³⁺ ions to realize the design of differential display in the fields of thermal response, time resolution, and luminescence color for optical anti-counterfeiting. The differentiated display can only be presented under specific multi-stimuli response, which further improves the security of information. Our work provides a new insight for designing advanced materials and can be expected to inspire future studies to explore optical anti-counterfeiting technology.

Investigation of the Solid-Solution Limit, Crystal Structure, and Thermal Quenching Mitigation of Sr-Substituted Rb2CaP2O7:Eu2+ Phosphors for White LED Applications

Rb₂CaP₂O₇:Eu²⁺ is a bright reddish-orange-emitting phosphor, but its luminescence thermal stability is poor. In this study, we investigated the solid-solution limit and thermal quenching mitigation of Rb₂CaP₂O₇:Eu²⁺ phosphors by cation substitution with Sr²⁺ and revisited their crystal structure. First, we carefully investigated the solid solution limit of Sr in the structure of Rb₂CaP₂O₇. The results show that up to 80% of Ca can be substituted by Sr, whereas Ca hardly resides in the structure of Rb₂SrP₂O₇. Consequently, the photoluminescence was fine-tuned from reddish-orange (612 nm) to yellow (580 nm) light emission by increasing the Sr²⁺ concentration in the solid-solution phosphors Rb₂Sr_1-xCa_xP₂O₇:Eu²⁺ under excitation at 342 nm. The mechanism for the blue shift of the emission spectrum was discussed. With the associated modification of the local environment of the activator (as reflected by the changes in the effective coordination number, average bond length, distortion index, and quadratic elongation), the luminescence thermal quenching issue of Rb₂CaP₂O₇:Eu²⁺ was mitigated by substituting 20% Sr into the Ca site (Rb₂Ca_0.8Sr_0.2P₂O₇:Eu²⁺). The integrated intensity of bright orange-emitting Rb₂Ca_0.8Sr_0.2P₂O₇:Eu²⁺ (603 nm) at 150 °C retained 53% of its initial value, 1.64 times that of Rb₂CaP₂O₇:Eu²⁺ (32.3%). Such an enhancement could be attributed to the improved rigidity of the crystal structure due to the local structure modification as evidenced by Rietveld refinement. The cation substitution is an effective approach for mitigating the thermal quenching issue of phosphors.

Advances in engineering near-infrared luminescent materials

Near-infrared (NIR) luminescent materials have emerged as a growing field of interest, particularly for imaging and optics applications in biology, chemistry, and physics. However, the development of materials for this and other use cases has been hindered by a range of issues that prevents their widespread use beyond benchtop research. This review explores emerging trends in some of the most promising NIR materials and their applications. In particular, we focus on how a more comprehensive understanding of intrinsic NIR material properties might allow researchers to better leverage these traits for innovative and robust applications in biological and physical sciences.

Machine Learning-Enabled Design and Prediction of Protein Resistance on Self-Assembled Monolayers and Beyond

The rational design of highly antifouling materials is crucial for a wide range of fundamental research and practical applications. The immense variety and complexity of the intrinsic physicochemical properties of materials (i.e., chemical structure, hydrophobicity, charge distribution, and molecular weight) and their surface coating properties (i.e., packing density, film thickness and roughness, and chain conformation) make it challenging to rationally design antifouling materials and reveal their fundamental structure-property relationships. In this work, we developed a data-driven machine learning model, a combination of factor analysis of functional group (FAFG), Pearson analysis, random forest (RF) and artificial neural network (ANN) algorithms, and Bayesian statistics, to computationally extract structure/chemical/surface features in correlation with the antifouling activity of self-assembled monolayers (SAMs) from a self-construction data set. The resultant model demonstrates the robustness of Q_CV² = 0.90 and RMSE_CV = 0.21 and the predictive ability of Q_ext² = 0.84 and RMSE_ext = 0.28, determines key descriptors and functional groups important for the antifouling activity, and enables to design original antifouling SAMs using the predicted antifouling functional groups. Three computationally designed molecules were further coated onto the surfaces in different forms of SAMs and polymer brushes. The resultant coatings with negative fouling indexes exhibited strong surface resistance to protein adsorption from undiluted blood serum and plasma, validating the model predictions. The data-driven machine learning model demonstrates their design and predictive capacity for next-generation antifouling materials and surfaces, which hopefully help to accelerate the discovery and understanding of functional materials.

Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence

Luminescent materials are continually sought for application in solid-state LED-based lighting and display applications. This has traditionally required extensive experimental effort. More recently, the employment of data-driven approaches in materials science has provided an alternative avenue to accelerate the discovery and development of luminescent materials. In this Perspective, we give an overview of luminescent materials used for lighting and display applications with a specific focus on inorganic phosphors, quantum dots, and organic light-emitting diodes. We discuss recent progress using data-driven approaches to discover new compounds, predict optical properties, and optimize synthesis, among other topics for each type of material. We then highlight future research directions focusing on using artificial intelligence (AI) to advance these fields and address some cross-cutting challenges limiting the current application of AI techniques in luminescence-related research.