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oai:figshare.com:article/34044468 2026-10-01T11:17:25Z category_146 category_915 category_7 category_12 category_734 category_931 portal_63 item_type_6 month_year_10_2026

Al-Stoichiometry-Engineered Defect Profiles in InAlZnO Nanofibers for Balanced Charge Transport and Optical Plasticity Xudong Zhang (420437) Xiangze Wang (11183819) Xianglong Wang (3726340) Xiaoyang Song (11856386) Haiyang Yu (543050) Hengzhen Zhang (25156242) Ruxin Xue (25156245) Zhen Liu (74646) Yuanbin Qin (10042384) Fengyun Wang (1530358) Space Science Physical Sciences not elsewhere classified Medicine Cell Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified related donor states pulsed uv light pulse facilitation index optimized device shows balanced charge transport 5s transport network cation stoichiometry control strong al – 08 </ sub low dark current 1 </ sub engineered defect profiles 2 </ sup carrier transport x </ state current 2 h 1 atomic tune sub sub >< photocarrier relaxation peak photocurrent neuromorphic vision nanofiber networks inalzno nanofibers effect mobility bandgap defects assisted trapping ></ sub 271 %. 14 cm Metal oxide optoelectronic synapses are promising for neuromorphic vision, but combining a low dark current with sustained photoinduced plasticity remains challenging. Here, Al stoichiometry is engineered in electrospun In₁Al_<i>x</i>Zn_0.08O (<i>x</i> = 0.007, 0.011, and 0.015) nanofiber networks to tune sub-bandgap defects, carrier transport, and photocarrier relaxation. Optical, spectroscopic, electrical, and synaptic measurements identify 1.1 atomic % Al as optimal. Strong Al–O bonding suppresses excess oxygen-vacancy-related donor states while preserving the In 5s transport network. The optimized device shows an off-state current of 10^–11 A, an on/off ratio of 10⁷, a field-effect mobility of 14 cm² V^–1 s^–1, and a paired-pulse facilitation index of 271%. Under pulsed UV light, defect-assisted trapping and slow relaxation retain 30% of the peak photocurrent after 2 h. Neural network simulations achieve 95.7% handwritten digit recognition accuracy, highlighting the value of cation stoichiometry control for low-power optoelectronic neuromorphic computing. 2026-10-01T00:00:00Z Text Journal contribution 10.1021/acs.jpclett.6c02945.s001 https://figshare.com/articles/journal_contribution/Al-Stoichiometry-Engineered_Defect_Profiles_in_InAlZnO_Nanofibers_for_Balanced_Charge_Transport_and_Optical_Plasticity/34044468 CC BY-NC 4.0