Literature DB >> 17640024

Combinatorial and high-throughput materials science.

Wilhelm F Maier1, Klaus Stöwe, Simone Sieg.   

Abstract

There is increasing acceptance of high-throughput technologies for the discovery, development, and optimization of materials and catalysts in industry. Over the years, the relative synchronous development of technologies for parallel synthesis and characterization has been accompanied by developments in associated software and information technologies. This Review aims to provide a comprehensive overview on the state of the art of the field by selected examples. Technologies developed to aid research on complex materials are covered as well as databases, design of experiment, data-mining technologies, modeling approaches, and evolutionary strategies for development. Different methods for parallel synthesis provide single sample libraries, gradient libraries for electronic or optical materials, similar to polymers and catalysts, and products produced through formulation strategies. Many examples illustrate the variety of isolated solutions and document the barely recognized variety of new methods for the synthesis and analysis of almost any material. The Review ends with a summary of success stories and statements on still-present problems and future tasks.

Entities:  

Year:  2007        PMID: 17640024     DOI: 10.1002/anie.200603675

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  12 in total

1.  Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

Authors:  Rama K Vasudevan; Kamal Choudhary; Apurva Mehta; Ryan Smith; Gilad Kusne; Francesca Tavazza; Lukas Vlcek; Maxim Ziatdinov; Sergei V Kalinin; Jason Hattrick-Simpers
Journal:  MRS Commun       Date:  2019       Impact factor: 2.566

2.  Catalyst discovery through megalibraries of nanomaterials.

Authors:  Edward J Kluender; James L Hedrick; Keith A Brown; Rahul Rao; Brian Meckes; Jingshan S Du; Liane M Moreau; Benji Maruyama; Chad A Mirkin
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-17       Impact factor: 11.205

3.  The high-throughput highway to computational materials design.

Authors:  Stefano Curtarolo; Gus L W Hart; Marco Buongiorno Nardelli; Natalio Mingo; Stefano Sanvito; Ohad Levy
Journal:  Nat Mater       Date:  2013-03       Impact factor: 43.841

4.  Evolution of catalysts directed by genetic algorithms in a plug-based microfluidic device tested with oxidation of methane by oxygen.

Authors:  Jason E Kreutz; Anton Shukhaev; Wenbin Du; Sasha Druskin; Olafs Daugulis; Rustem F Ismagilov
Journal:  J Am Chem Soc       Date:  2010-03-10       Impact factor: 15.419

Review 5.  Synthesis at the interface of chemistry and biology.

Authors:  Xu Wu; Peter G Schultz
Journal:  J Am Chem Soc       Date:  2009-09-09       Impact factor: 15.419

Review 6.  Materials for stem cell factories of the future.

Authors:  Adam D Celiz; James G W Smith; Robert Langer; Daniel G Anderson; David A Winkler; David A Barrett; Martyn C Davies; Lorraine E Young; Chris Denning; Morgan R Alexander
Journal:  Nat Mater       Date:  2014-06       Impact factor: 43.841

7.  Single-Layered Microfluidic Network-Based Combinatorial Dilution for Standard Simplex Lattice Design.

Authors:  Kangsun Lee; Choong Kim; Kwang W Oh
Journal:  Micromachines (Basel)       Date:  2018-09-25       Impact factor: 2.891

8.  Accelerating High-Throughput Screening for Structural Materials with Production Management Methods.

Authors:  Alexander Bader; Finn Meiners; Kirsten Tracht
Journal:  Materials (Basel)       Date:  2018-08-01       Impact factor: 3.623

9.  Combinatorial techniques to efficiently investigate and optimize organic thin film processing and properties.

Authors:  Florian Wieberger; Tristan Kolb; Christian Neuber; Christopher K Ober; Hans-Werner Schmidt
Journal:  Molecules       Date:  2013-04-08       Impact factor: 4.411

Review 10.  Progress and prospects for accelerating materials science with automated and autonomous workflows.

Authors:  Helge S Stein; John M Gregoire
Journal:  Chem Sci       Date:  2019-09-20       Impact factor: 9.825

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