Enhancing Nonlinear Optical Absorption in SnS<sub>2</sub> Through Electrostatic Doping for Optical Neural Network Applications

dc.contributor.authorKe, Yutingen
dc.contributor.authorBoukhvalov, Danil W.en
dc.contributor.authorHumphrey, Mark G.en
dc.contributor.authorZhang, Chien
dc.contributor.authorHuang, Zhipengen
dc.date.accessioned2026-02-26T23:40:28Z
dc.date.available2026-02-26T23:40:28Z
dc.date.issued2025-10-26en
dc.description.abstractThe development of high-performance nonlinear optical (NLO) materials is crucial for advancing photoelectric devices, particularly optical nonlinear activation units in optical neural networks (ONNs), yet it remains a significant challenge. In this work, it is demonstrated that electrostatic doping offers a versatile strategy to enhance the NLO performance of two-dimensional materials, with promising implications for ONN applications. As a proof of concept, we employed proton (H⁺) intercalation to dope SnS2. Spectroscopic characterizations, including Raman, X-ray photoelectron spectroscopy, and electron paramagnetic resonance, confirm successful electrostatic doping with negligible lattice expansion or defect generation. The doped SnS2 exhibits enhanced saturable absorption (SA), two-photon absorption (2PA), or three-photon absorption (3PA) under femtosecond laser excitation across a broad wavelength range (515–1550 nm). The enhancement in SA is attributed to increased electron population in the conduction band that strengthens the Pauli blocking effect, while the improvements in 2PA and 3PA arise from the internal electric field generated by intercalated H⁺ ions within the van der Waals gaps and accumulated electrons in SnS2. The application potential of H⁺-intercalated SnS2 is further validated in a modeled ONN, where it achieves digit recognition accuracy comparable to that of conventional electronic activation functions.en
dc.description.sponsorshipZ.H. acknowledges the support from the National Natural Science Foundation of China (Nos. 62275197 and 51772214), the Natural Science Foundation of Shanghai (23ZR1465700), the National Youth Talent Support Program of China (No. W03070073), and the Fundamental Research Funds for the Central Universities. C.Z. acknowledges support from the National Natural Science Foundation of China (No. 51432006), the Ministry of Education of China for the Changjiang Innovation Research Team (No. IRT14R23), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (No. B13025), and the Innovation Program of Shanghai Municipal Education Commission. D.W. Boukhvalov acknowledges support from the Jiangsu Innovative and Entrepreneurial Talents Project and the Ministry of Science and Education of the Russian Federation (Project FEUZ-2023-0013). M.G. Humphrey thanks the Australian Research Council (DP170100411). Z.H. acknowledges the support from the National Natural Science Foundation of China (Nos. 62275197 and 51772214), the Natural Science Foundation of Shanghai (23ZR1465700), the National Youth Talent Support Program of China (No. W03070073), and the Fundamental Research Funds for the Central Universities. C.Z. acknowledges support from the National Natural Science Foundation of China (No. 51432006), the Ministry of Education of China for the Changjiang Innovation Research Team (No. IRT14R23), the Ministry of Education and the State Administration of Foreign Experts Affairs for the 111 Project (No. B13025), and the Innovation Program of Shanghai Municipal Education Commission. D.W. Boukhvalov acknowledges support from the Jiangsu Innovative and Entrepreneurial Talents Project and the Ministry of Science and Education of the Russian Federation (Project FEUZ‐2023‐0013). M.G. Humphrey thanks the Australian Research Council (DP170100411).en
dc.description.statusPeer-revieweden
dc.format.extent10en
dc.identifier.otherORCID:/0000-0002-4433-6783/work/206554212en
dc.identifier.scopus105019755679en
dc.identifier.urihttps://hdl.handle.net/1885/733806679
dc.language.isoenen
dc.rights©2025 The authorsen
dc.sourceAdvanced Optical Materialsen
dc.subject2D semiconductorsen
dc.subjectbuilt-in electric fielden
dc.subjectelectrostatic dopingen
dc.subjectnonlinear optical materialsen
dc.subjectoptical neural networksen
dc.titleEnhancing Nonlinear Optical Absorption in SnS<sub>2</sub> Through Electrostatic Doping for Optical Neural Network Applicationsen
dc.typeJournal articleen
dspace.entity.typePublicationen
local.contributor.affiliationKe, Yuting; Tongji Universityen
local.contributor.affiliationBoukhvalov, Danil W.; Nanjing Forestry Universityen
local.contributor.affiliationHumphrey, Mark G.; Chemistry Research, Research School of Chemistry, ANU College of Science and Medicine, The Australian National Universityen
local.contributor.affiliationZhang, Chi; Tongji Universityen
local.contributor.affiliationHuang, Zhipeng; Tongji Universityen
local.identifier.citationvolume13en
local.identifier.doi10.1002/adom.202502300en
local.identifier.purea37e3abf-a8ae-4ced-a2f0-efdcac89b880en
local.identifier.urlhttps://www.scopus.com/pages/publications/105019755679en
local.type.statusPublisheden

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