A Search for New Red Phosphors Using a Computational Evolutionary Optimization Process

Abstract

A computational evolutionary optimization process is adopted to screen a Eu3+‐doped alkali earth borosilicate system in an attempt to search for red phosphors with a high luminescent efficiency when excited by soft ultraviolet irradiation. The evolutionary optimization process involves a genetic algorithm and combinatorial chemistry, which was tailored for the development of light‐emitting diode (LED) phosphors. Vertical simulations and an actual synthesis have been carried out in the present investigation, and promising red phosphors for three‐band white LED applications have been obtained.

Luminescence properties of (Sr0.95−x−yMgxYy)Si2O2−yN2+y:Eu2+ 0.05 for novel LED conversion phosphors

“…Generally, with the varying of the amount of Eu 2+ incorporated into the host lattice, the local surroundings of a substituted site will significantly change its bonding length, angle, and point symmetry etc, which eventually makes it possible to improve or tune the luminescence properties [24][25][26][27]. In a similar way, tuning can also be achieved by the replacement of Sr with Mg and/or Y.…”

Facile synthesis of lanthanide vanadates and their luminescent properties

“…It is also known that the half-filled shell ground state of the gadolinium leads to the phosphors with lowest excited levels and relatively high energy [6,7]. The encapsulated Eu 3+ ions with the parity forbidden characteristics usually exhibits strong red line emissions [7,8], The resultant composite has high color purity and can be used for the fabrication of red phosphors [9,10].…”

Phosphors by Design: Approaches Toward the Development of Advanced Luminescent Materials

“…The ≈700 compounds prepared for the development of this phosphor are far less than the 25,000 samples required in the pure combinatorial work indicating the efficiency of this hybrid approach. 38 This approach was also used in other systems. 40,41 With the initial success of these techniques, their effectiveness has been further enhanced using the multi-objective optimization process (MOP), for example non-dominated sorting genetic algorithms (NSGA), 42 Particle Swarm Optimization (PSO), 43 and elitisminvolved non-dominated sorting genetic algorithms (NSGA-II).…”

“…39 For example, using GACC, a new phosphor was identified in a complex phase space containing seven cations (Eu, Mg, Ca, Sr, Ba, B, Si). 38 Employing a scanning multiinjection delivery system, a library of 108 compositions was created as the first generation. After the non-ordered, randomized first generation was optically characterized, GACC was applied to compare the resulting luminance and then inform subsequent generations.…”

“…27,34 These computational and statistic algorisms are all applied to minimize materials synthesis. [35][36][37][38] In phosphor development, genetic-algorithm-assisted combinatorial chemistry (GACC) is used to significantly reduce the number of samples by focusing the search through genetic algorithms. Incorporating the evolution of species (inheritance, mutation, selection, crossover) from a random starting generation, each subsequent generation is improved while the overall number of required syntheses is reduced.…”