Sub-photon accuracy noise reduction of a single shot coherent diffraction pattern with an atomic model trained autoencoder.

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning.

Fast Response Organic Supramolecular Transistors Utilizing In-Situ π-Ion Gels.

Despite their remarkable charge carrier mobility when forming well-ordered fibers, supramolecular transistors often suffer from poor processability that hinders device integration, resulting in disappointing transconductance and output currents. Here, a new class of supramolecular transistors, π-ion gel transistors (PIGTs), is presented. An in situ π-ion gel, which is an unprecedented composite of semiconducting nanofibers and an enclosed ionic liquid, is directly employed as an active material and internal capacitor. In comparison to other supramolecular transistors, a PIGT displays a high transconductance (133 µS) and output current (139 µA at -6 V), while retaining a high charge-carrier mobility (4.2 × 10-2 cm2 V-1 s-1 ) and on/off ratio (3.7 × 104 ). Importantly, the unique device configuration and the high ionic conductivity associated with the distinct nanosegregation enables the fastest response among accumulation-mode electrochemical-based transistors (<20 µs). Considering the advantages of the absence of dielectric layers and the facile fabrication process, PIGT has great potential to be utilized in printed flexible devices. The device platform is widely applicable to various supramolecular assemblies, shedding light on the interdisciplinary research of supramolecular chemistry and organic electronics.