Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications.
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| Title: | Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications. |
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| Authors: | Jayan K., Deepthi1 (AUTHOR) deepthij@rajagiritech.edu.in, Babu, Kesiya1 (AUTHOR) |
| Source: | Journal of Luminescence. Jan2025, Vol. 277, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Quantum computing, Electronic equipment, Optoelectronic devices, Quantum confinement effects, Quantum dots |
| Abstract: | Luminescent metal halide quantum dots (QDs), particularly perovskite quantum dots (PQDs), garnered remarkable attention for unique optical properties as well as critical use for advanced photonic and electronic devices. This comprehensive review explores the synthesis, properties, and applications of PQDs, with a focus on their role in luminescent metal halide QD devices. The review begins by discussing advanced synthesis techniques and surface engineering strategies for PQDs, highlighting recent developments in the field. Structural and optical characterization techniques are then examined, emphasizing the importance of understanding quantum confinement effects and emission mechanisms in PQDs. The review also includes a discussion on modelling and simulation, discussing computational methods for predicting and optimizing PQD properties. Experimental studies and device fabrication techniques are discussed in detail, showcasing the progress made in integrating PQDs into optoelectronic devices. Advanced applications of PQDs in light-emitting devices, solar cells, sensors, and photodetectors are explored, highlighting their potential for efficiency enhancements and novel functionalities. A detailed discussion on the emerging role of machine learning (ML) in PQD research, focusing on its applications in materials discovery and device optimization are also included. This review explores the potential of luminescent PQDs for quantum computing applications, focusing on their role as qubits, quantum gates, and quantum memory devices, emphasizing the latest advancements, challenges, and future prospects of integrating PQDs into quantum computing architectures. The review concludes with an overview of emerging trends and future directions in the field, emphasizing the need for continued research to unlock the full potential of PQDs in advanced photonic and electronic devices. • Covers advanced synthesis, surface engineering, and characterization studies for PQDs, focusing recent advancements. • Includes modeling and simulation for optimizing PQD properties, along with experimental studies and device fabrication. • Explores PQD applications in LEDs, solar cells, sensors, and photodetectors for enhanced efficiency and functionality. • Offers insight into ML applications in PQD research and their potential roles in quantum computing. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Luminescence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181540375 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jayan+K%2E%2C+Deepthi%22">Jayan K., Deepthi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> deepthij@rajagiritech.edu.in</i><br /><searchLink fieldCode="AR" term="%22Babu%2C+Kesiya%22">Babu, Kesiya</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Luminescence%22">Journal of Luminescence</searchLink>. Jan2025, Vol. 277, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+equipment%22">Electronic equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Optoelectronic+devices%22">Optoelectronic devices</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+confinement+effects%22">Quantum confinement effects</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+dots%22">Quantum dots</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Luminescent metal halide quantum dots (QDs), particularly perovskite quantum dots (PQDs), garnered remarkable attention for unique optical properties as well as critical use for advanced photonic and electronic devices. This comprehensive review explores the synthesis, properties, and applications of PQDs, with a focus on their role in luminescent metal halide QD devices. The review begins by discussing advanced synthesis techniques and surface engineering strategies for PQDs, highlighting recent developments in the field. Structural and optical characterization techniques are then examined, emphasizing the importance of understanding quantum confinement effects and emission mechanisms in PQDs. The review also includes a discussion on modelling and simulation, discussing computational methods for predicting and optimizing PQD properties. Experimental studies and device fabrication techniques are discussed in detail, showcasing the progress made in integrating PQDs into optoelectronic devices. Advanced applications of PQDs in light-emitting devices, solar cells, sensors, and photodetectors are explored, highlighting their potential for efficiency enhancements and novel functionalities. A detailed discussion on the emerging role of machine learning (ML) in PQD research, focusing on its applications in materials discovery and device optimization are also included. This review explores the potential of luminescent PQDs for quantum computing applications, focusing on their role as qubits, quantum gates, and quantum memory devices, emphasizing the latest advancements, challenges, and future prospects of integrating PQDs into quantum computing architectures. The review concludes with an overview of emerging trends and future directions in the field, emphasizing the need for continued research to unlock the full potential of PQDs in advanced photonic and electronic devices. • Covers advanced synthesis, surface engineering, and characterization studies for PQDs, focusing recent advancements. • Includes modeling and simulation for optimizing PQD properties, along with experimental studies and device fabrication. • Explores PQD applications in LEDs, solar cells, sensors, and photodetectors for enhanced efficiency and functionality. • Offers insight into ML applications in PQD research and their potential roles in quantum computing. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Luminescence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jlumin.2024.120906 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Quantum computing Type: general – SubjectFull: Electronic equipment Type: general – SubjectFull: Optoelectronic devices Type: general – SubjectFull: Quantum confinement effects Type: general – SubjectFull: Quantum dots Type: general Titles: – TitleFull: Luminescent perovskite quantum dots: Progress in fabrication, modelling and machine learning approaches for advanced photonic and quantum computing applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jayan K., Deepthi – PersonEntity: Name: NameFull: Babu, Kesiya IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00222313 Numbering: – Type: volume Value: 277 Titles: – TitleFull: Journal of Luminescence Type: main |
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