Multi-Layered Triboelectric Nanogenerators with Controllable Multiple Spikes for Low-Power Artificial Synaptic Devices

In the domains of wearable electronics, robotics, and the Internet of Things, there is a demand for devices with low power consumption and the capability of multiplex sensing, memory, and learning. Triboelectric nanogenerators (TENGs) offer remarkable versatility in this regard, particularly when integrated with synaptic transistors that mimic biological synapses. However, conventional TENGs, generating only two spikes per cycle, have limitations when used in synaptic devices requiring repetitive high-frequency gating signals to perform various synaptic plasticity functions. Herein, a multi-layered micropatterned TENG (M-TENG) consisting of a polydimethylsiloxane (PDMS) film and a composite film that includes 1H,1H,2H,2H-perfluorooctyltrichlorosilane/BaTiO3 /PDMS are proposed. The M-TENG generates multiple spikes from a single touch by utilizing separate triboelectric charges at the multiple friction layers, along with a contact/separation delay achieved by distinct spacers between layers. This configuration allows the maximum triboelectric output charge of M-TENG to reach up to 7.52 nC, compared to 3.69 nC for a single-layered TENG. Furthermore, by integrating M-TENGs with an organic electrochemical transistor, the spike number multiplication property of M-TENGs is leveraged to demonstrate an artificial synaptic device with low energy consumption. As a proof-of-concept application, a robotic hand is operated through continuous memory training under repeated stimulations, successfully emulating long-term plasticity.

Giant Spin-Orbit Torque in Sputter-Deposited Bi Films

Bismuth (Bi) has the strongest spin-orbit coupling among non-radioactive elements and is thus a promising material for efficient charge-to-spin conversion. However, previous electrical detections have reported controversial results for the conversion efficiency. In this study, an optical detection of a spin-orbit torque is reported in a Bi/CoFeB bilayer with a polycrystalline texture of (012) and (003). Taking advantage of the optical detection, spin-orbit torque is accurately separated from the Oersted field and achieves a giant damping-like torque efficiency of +0.5, verifying efficient charge-to-spin conversion. This study also demonstrates a field-like torque efficiency of -0.1. For the mechanism of the charge-to-spin conversion, the bulk spin Hall effect and the interface Rashba-Edelstein effect are considered.

Correlation between Redox Potential and Solvation Structure in Biphasic Electrolytes for Li Metal Batteries

The activity of lithium ions in electrolytes depends on their solvation structures. However, the understanding of changes in Li⁺ activity is still elusive in terms of interactions between lithium ions and solvent molecules. Herein, the chelating effect of lithium ion by forming [Li(15C5)]⁺ gives rise to a decrease in Li⁺ activity, leading to the negative potential shift of Li metal anode. Moreover, weakly solvating lithium ions in ionic liquids, such as [Li(TFSI)₂ ]^- (TFSI = bis(trifluoromethanesulfonyl)imide), increase in Li⁺ activity, resulting in the positive potential shift of LiFePO₄ cathode. This allows the development of innovative high energy density Li metal batteries, such as 3.8 V class Li | LiFePO₄ cells, along with introducing stable biphasic electrolytes. In addition, correlation between Li⁺ activity, cell potential shift, and Li⁺ solvation structure is investigated by comparing solvated Li⁺ ions with carbonate solvents, chelated Li⁺ ions with cyclic and linear ethers, and weakly solvating Li⁺ ions in ionic liquids. These findings elucidate a broader understanding of the complex origin of Li⁺ activity and provide an opportunity to achieve high energy density lithium metal batteries.

Optimal Composition of Li Argyrodite with Harmonious Conductivity and Chemical/Electrochemical Stability: Fine-Tuned Via Tandem Particle Swarm Optimization

A tandem (two-step) particle swarm optimization (PSO) algorithm is implemented in the argyrodite-based multidimensional composition space for the discovery of an optimal argyrodite composition, i.e., with the highest ionic conductivity (7.78 mS cm^-1 ). To enhance the industrial adaptability, an elaborate pellet preparation procedure is not used. The optimal composition (Li_5.5 PS_4.5 Cl_0.89 Br_0.61 ) is fine-tuned to enhance its practical viability by incorporating oxygen in a stepwise manner. The final composition (Li_5.5 PS_4.23 O_0.27 Cl_0.89 Br_0.61 ), which exhibits an ionic conductivity (σ_ion ) of 6.70 mS cm^-1 and an activation barrier of 0.27 eV, is further characterized by analyzing both its moisture and electrochemical stability. Relative to the other compositions, the exposure of Li_5.5 PS_4.23 O_0.27 Cl_0.89 Br_0.61 to a humid atmosphere results in the least amount of H₂ S released and a negligible change in structure. The improvement in the interfacial stability between the Li(Ni_0.9 Co_0.05 Mn_0.05 )O₂ cathode and Li_5.5 PS_4.23 O_0.27 Cl_0.89 Br_0.61 also results in greater specific capacity during fast charge/discharge. The structural and chemical features of Li_5.5 PS_4.5 Cl_0.89 Br_0.61 and Li_5.5 PS_4.23 O_0.27 Cl_0.89 Br_0.61 argyrodites are characterized using synchrotron X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy. This work presents a novel argyrodite composition with favorably balanced properties while providing broad insights into material discovery methodologies with applications for battery development.

A Learning-Rate Modulable and Reliable TiOx Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO_x ) memristor array devices are fabricated to be utilized as constituent device element in hardware neural network, representing passive matrix array structure enabling vector-matrix multiplication process between multisignal and trained synaptic weight. In particular, in situ convolutional neural network hardware system is designed and implemented using a multiple 25 × 25 TiO_x memristor arrays and the memristor device parameters are developed to bring global constant voltage programming scheme for entire cells in crossbar array without any voltage tuning peripheral circuit such as transistor. Moreover, the learning rate modulation during in situ hardware training process is successfully achieved due to superior TiO_x memristor performance such as threshold uniformity (≈2.7%), device yield (> 99%), repetitive stability (≈3000 spikes), low asymmetry value of ≈1.43, ambient stability (6 months), and nonlinear pulse response. The learning rate modulable fast-converging in situ training based on direct memristor operation shows five times less training iterations and reduces training energy compared to the conventional hardware in situ training at ≈95.2% of classification accuracy.

Phase Matching via Plasmonic Modal Dispersion for Third Harmonic Generation

The overall effectiveness of nonlinear optical processes along extended nonlinear media highly depends on the fulfillment of the phase-matching condition for pump and generated fields. This is traditionally accomplished by exploiting the birefringence of nonlinear crystals requiring long interaction lengths (cm-scale). For nonbirefringent media and integrated photonic devices, modal phase matching can compensate the index mismatch. Here, the various interacting waves propagate in transverse modes with appropriate phase velocities, but they suffer from a low refractive index contrast and cm-scale interaction lengths. This work harnesses modal phase matching for third-harmonic generation (THG) in plasmonic waveguides using an organic polymer (poly[3-hexylthiophene-2,5-diyl]) as the nonlinear medium. One demonstrates experimentally an effective interaction area as small as ≈ 0.11 µm² and the phase-matched modal dispersion results in THG efficiency as high as ≈ 10^-3 W^-2 within an effective length scale of ≈ 4.3 µm. THG also shows a strong correlation with the polarization of the incident laser beam, corresponding to the excitation of the antisymmetric plasmonic modes, corroborating that plasmonic modal phase matching is achieved. This large reduction in device area of orders of magnitude is interesting for various applications where space is critical (e.g., device integration or on-chip applications).

Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image Learning

The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiO_x memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (P_Act ) from 0 to 1.0, and can even modulate the degree of the P_Act change. A drop-connected algorithm based on the P_Act is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.

Bleaching-Resistant Super-Resolution Fluorescence Microscopy

Photobleaching is the permanent loss of fluorescence after extended exposure to light and is a major limiting factor in super-resolution microscopy (SRM) that restricts spatiotemporal resolution and observation time. Strategies for preventing or overcoming photobleaching in SRM are reviewed developing new probes and chemical environments. Photostabilization strategies are introduced first, which are borrowed from conventional fluorescence microscopy, that are employed in SRM. SRM-specific strategies are then highlighted that exploit the on-off transitions of fluorescence, which is the key mechanism for achieving super-resolution, which are becoming new routes to address photobleaching in SRM. Off states can serve as a shelter from excitation by light or an exit to release a damaged probe and replace it with a fresh one. Such efforts in overcoming the photobleaching limits are anticipated to enhance resolution to molecular scales and to extend the observation time to physiological lifespans.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

Soluble ICAM-1 a Pivotal Communicator between Tumors and Macrophages, Promotes Mesenchymal Shift of Glioblastoma

Despite aggressive clinical treatment, recurrence of glioblastoma multiforme (GBM) is unavoidable, and the clinical outcome is still poor. A convincing explanation is the phenotypic transition of GBM cells upon aggressive treatment such as radiotherapy. However, the microenvironmental factors contributing to GBM recurrence after treatment remain unexplored. Here, it is shown that radiation-treated GBM cells produce soluble intercellular adhesion molecule-1 (sICAM-1) which stimulates the infiltration of macrophages, consequently enriching the tumor microenvironment with inflammatory macrophages. Acting as a paracrine factor, tumor-derived sICAM-1 induces macrophages to secrete wingless-type MMTV integration site family, member 3A (WNT3A), which promotes a mesenchymal shift of GBM cells. In addition, blockade of either sICAM-1 or WNT3A diminishes the harmful effect of radiation on tumor progression. Collectively, the findings indicate that cellular crosstalk between GBM and macrophage through sICAM-1-WNT3A oncogenic route is involved in the mesenchymal shift of GBM cells after radiation, and suggest that radiotherapy combined with sICAM-1 targeted inhibition would improve the clinical outcome of GBM patients.