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Engineering Conjugated Bridges in TPE-BT-Based Donor–Acceptor Molecules for Optimized Resistive Random Access Memory

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Figshare2026-04-28 收录
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https://figshare.com/articles/dataset/Engineering_Conjugated_Bridges_in_TPE-BT-Based_Donor_Acceptor_Molecules_for_Optimized_Resistive_Random_Access_Memory/28920781
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Four donor–acceptor (D-A) type organic small molecules, namely, 4,7-bis­(4-(1,2,2-triphenylvinyl)­phenyl)­benzo­[c]­[1,2,5]­thiadiazole­(TPE-BT), 4,7-bis­((4-(1,2,2-triphenylvinyl)­phenyl)­ethynyl)­benzo­[c]­[1,2,5]­thiadiazole­(TPE-ynl-BT), 4,7-bis­(5-(4-(1,2,2-triphenylvinyl)­phenyl)­thiophen-2-yl)­benzo­[c]­[1,2,5]­thiadiazole (TPE-Th-BT), and 4,7-bis­((5-(4-(1,2,2-triphenylvinyl)­phenyl)­thiophen-2yl)­ethynyl)­benzo­[c]­[1,2,5]­thiadiazole­(TPE-Th-ynl-BT), each incorporating unique conjugated bridges, are designed, synthesized, and integrated into resistive random access memory (RRAM) devices. Current–voltage (I–V) measurements indicate that the TPE-BT, TPE-ynl-BT and TPE-Th-BT based devices exhibit write-once-read-many-times (WORM) characteristics, while TPE-Th-ynl-BT based devices show a stable flash-type switching behavior. In comparison to TPE-BT, the memory devices constructed with TPE-ynl-BT, TPE-Th-BT and TPE-Th-ynl-BT, which include additional conjugated bridges, exhibit nonvolatile memory capabilities with reduced threshold voltages, higher ION/IOFF (104:1), enhanced stability, and improved reproducibility. The photophysical, electrochemical analyses, and X-ray diffraction (XRD) results reveal that incorporating conjugated bridges within molecular structures can enhance data storage performance while reducing power consumption. Our findings demonstrate that these conjugated bridges play a crucial role in optimizing electrical memory characteristics and resistive switching behavior. Moreover, the device fabricated with TPE-Th-ynl-BT is effectively applied to logic gate circuits and American Standard Code for Information Interchange (ASCII) art function, highlighting its promising potential as a smart sensor within artificial intelligence (AI) networks.
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