Temperature driven p–n–p type conduction switching materials: current trends and future directions

Literature Information

Publication Date 2015-03-19
DOI 10.1039/C4CP06088A
Impact Factor 3.676
Authors

Satya N. Guin, Kanishka Biswas


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Abstract

Modern technological inventions have been going through a “renaissance” period. Development of new materials and understanding of fundamental structure–property correlations are the important steps to move further for advanced technologies. In modern technologies, inorganic semiconductors are the leading materials which are extensively used for different applications. In the current perspective, we present discussion on an important class of materials that show fascinating p–n–p type conduction switching, which can have potential applications in diodes or transistor devices that operate reversibly upon temperature or voltage change. We highlight the key concepts, present the current fundamental understanding and show the latest developments in the field of p–n–p type conduction switching. Finally, we point out the major challenges and opportunities in this field.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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