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FRINATEK-Fri prosj.st. mat.,naturv.,tek

High entropy alloys for thermal, electronic and optical applications

Alternativ tittel: Høyentropilegeringer for termiske, elektroniske og optiske anvendelser

Tildelt: kr 9,9 mill.

Det er behov for nye halvledermaterialer på mange områder innen fornybar energi, energisparing og elektronikk. Én gruppe materialer har hittil unngått oppmerksomhet som potensielle halvledere: høyentropilegeringer (HEA). De består av minst fem ulike metalliske grunnstoffer i like store mengder, har enkle strukturer og kan vise uvanlige og lovende elektroniske egenskaper. Hittil har man imidlertid lagt vekt på strukturelle anvendelser på grunn av HEAs styrke, korrosjonsbestandighet og stabilitet. Målet med HEATER var å identifisere nye halvledere basert på HEA. Metoden som ble brukt, var basert på et systematisk high-throughput-søk i et stort antall eksisterende og hypotetiske HEA-sammensetninger etter egenskaper som er relevante for halvlederteknologi. Ikke bare er antallet kombinasjoner av grunnstoffer stort, men friheten til å variere mengden av hvert grunnstoff rundt den ekvimolare mengden (f.eks. rundt 20 % for fem grunnstoffer) gjør at antallet muligheter eksploderer. Dette krevde en systematisk tilnærming med høy gjennomstrømning, der egenskapene til mange sammensetninger ble undersøkt samtidig. I HEATER ble dette gjort både eksperimentelt og teoretisk. Ved hjelp av magnetronforstøvning med flere kilder ble det generert graderte filmer med tusenvis av sammensetninger på én enkelt skive. En rekke karakteriseringsteknikker med høy gjennomstrømning ble brukt for å kartlegge de fysiske egenskapene langs sammensetningsintervallene. Screeningen ble videreført med et maskinlæringskonsept, der intelligent dataanalyse ble brukt til å forutsi krystallsymmetrien til nye forbindelser. Det ble undersøkt store sammensetningsområder, inkludert oksider, nitrider, silikider og antimonider. Den største utfordringen i screeningprosessen var å sikre fasestabilitet, da de fleste sammensetningene tilsvarte flerfasemikrostrukturer. Ved hjelp av stabiliseringsstrategier har flere nye halvlederfaser som var helt ukjente for vitenskapen, blitt produsert og karakterisert med hensyn til transportegenskaper. De nye screeningstrategiene og maskinlæringsalgoritmene er implementert hos partnerne. Disse verktøyene er ekstremt viktige for fremtidig forskning og forventes å påvirke hele det vitenskapelige feltet.

HEATER explored high-entropy alloys and high-entropy compounds, such as oxides, nitrides, silicides and antimonides. Fundamental knowledge on complex alloys and compounds was acquired by the consortium as documented by the many communications and publications that resulted from the project. The work developed in the context of HEATER on the educated exploration of these materials and on high-throughput synthesis and characterization led to important research spin-offs such as the projects ANSWER (RCN 280545), Magnificent (RCN 287979), Allotherm (RCN 314778) and START (EU 101058632). Significant scientific outcomes of HEATER include: • Demonstration of entropy stabilization of several compounds. • New and unexpected crystal structures adopted by high-entropy compounds. • (Meta)stabilization of multiphase compositions through amorphization. • Identification of new semiconductors such as Fe10Co8Ni8Cu14Ge11O50 and Zn25Fe14Co12Ni8Mn8Sb33. • Some high entropy compounds showed exotic fundamental behavior, such as inversion from p-type to n-type conduction with temperature and exchange bias at room temperature. The HEATER project allowed the groups involved at SINTEF, UiO and NTNU to strengthen their knowledge in solid-state physics of high entropy alloys and compounds. In addition, a series of theoretical tools and experimental protocols have been developed, serving as the basis for future research. The research concept and its results were extensively disseminated during the course of the project as attested by the scientific communications and publications added to the Cristin repository. Notably, several popular science dissemination activities and one master thesis were also carried out in the framework of the project. The consortium expects to continue publishing results that stem from HEATER. In addition, a workshop with industrial stakeholders is planned for February 2024 from which new applied research is expected to arise. This workshop will be held jointly with other projects on high-entropy materials that are also led by SINTEF, such as ANSWER (RCN 280545), Magnificent (RCN 287979) and Allotherm (RCN 314778).

New seminconductor materials are required in many areas within renewable energy and energy saving as well as in electronics. One group of materials has so far avoided attention as potential semiconductors: high entropy alloys (HEA). They typically consist of at least five different metallic elements in approximately equimolar amounts and adopt simple crystal structures. HEA can exhibit unusual and promising properties for many applications, but up to now the main emphasis has been to use them as structural materials due to their strength, corrosion resistance and/or stability. The main idea of the present project is to initiate a systematic search for properties that are relevant for semiconductor-based technologies within the space of hypothetical HEA compositions. This space is enormous: not only is the number of combinatorial possibilities huge, but the freedom to vary the amount of each element around the equimolar quantity (e.g. 20% for five elements) explodes the number of potential compositions. This calls for a systematic, high-throughput approach where the properties of a large number of compositions can be investigated simultaneously. The HEATER project will do this both experimentally and theoretically. Multi-source magnetron sputtering will generate graded films with thousands of compositions represented in a single disk. A range of high-throughput characterization techniques will be used to map out several properties for composition ranges. Simultaneously, electronic scale modelling will be used to complement the experimental work using a host of state-of-the-art techniques. The optimization will be taken further with a machine learning concept, where intelligent data analysis is used to parameterize models that can predict compositions completely different from the usual suspects. If successful, this project will give a tremendous contribution to materials technology as well as to fundamental materials science.

Publikasjoner hentet fra Cristin

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FRINATEK-Fri prosj.st. mat.,naturv.,tek