Search for: All records

Abstract Computers, televisions, and smartphones are revolutionized by the invention of InGaN blue light‐emitting diode (LED) backlighting. Yet, continual exposure to the intense blue LED emission from these modern displays can cause insomnia and mood disorders. Developing “human‐centric” backlighting that uses a violet‐emitting LED chip and a trichromatic phosphor mixture to generate color images is one approach that addresses this problem. The challenge is finding a blue‐emitting phosphor that possesses a sufficiently small Stokes’ shift to efficiently down‐convert violet LED light and produce a narrow blue emission. This work reports a new oxynitride phosphor that meets this demand. K₃AlP₃O₉N:Eu²⁺ exhibits an unexpectedly narrow (45 nm, 2206 cm⁻¹), thermally robust, and efficient blue photoluminescence upon violet excitation. Computational modeling and temperature‐dependent optical property measurements reveal that the narrow emission arises from a rare combination of preferential excitation and site‐selective quenching. The resulting chromaticity coordinates of K₃AlP₃O₉N:Eu²⁺ lie closer to the vertex of the Rec. 2020 than a blue LED chip and provides access to ≈10% more colors than a commercial tablet when combined with commercial red‐ and green‐emitting phosphors. Alongside the wide gamut, tuning the emission from the violet LED and phosphor blend can reduce blue light emissions to produce next‐generation, human‐centric displays. 

Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiO_xdevices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO_xunder sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware. 

Abstract All‐solid‐state rechargeable sodium (Na)‐ion batteries are promising for inexpensive and high‐energy‐density large‐scale energy storage. In this contribution, new Na solid electrolytes, Na_3−yPS_4−xCl_x, are synthesized with a strategic approach, which allows maximum substitution of Cl for S (x= 0.2) without significant compromise of structural integrity or Na deficiency. A maximum conductivity of 1.96 mS cm⁻¹at 25 °C is achieved for Na_3.0PS_3.8Cl_0.2, which is two orders of magnitude higher compared with that of tetragonal Na₃PS₄(t‐Na₃PS₄). The activation energy (E_a) is determined to be 0.19 eV. Ab initio molecular dynamics simulations shed light on the merit of maximizing Cl‐doping while maintaining low Na deficiency in enhanced Na‐ion conduction. Solid‐state nuclear magnetic resonance (NMR) characterizations confirm the successful substitution of Cl for S and the resulting change of P oxidation state from 5+ to 4+, which is also verified by spin moment analysis. Ion transport pathways are determined with a tracer‐exchange NMR method. The functional detects that promote Na ‐ion transport are maximized for further improvement in ionic conductivity. Full‐cell performance is demonstrated using Na/Na_3.0PS_3.8Cl_0.2/Na₃V₂(PO₄)₃with a reversible capacity of ≈100 mAh g^‐1at room temperature.