Quasi-Homogeneous Photocatalysis in Ultrastiff Microporous Polymer Aerogels.

The development of efficient and low-cost catalysts is essential for photocatalysis; however, the intrinsically low photocatalytic efficiency as well as the difficulty in using and recycling photocatalysts in powder morphology greatly limit their practical performance. Herein, we describe quasi-homogeneous photocatalysis to overcome these two limitations by constructing ultrastiff, hierarchically porous, and photoactive aerogels of conjugated microporous polymers (CMPs). The CMP aerogels exhibit low density but high stiffness beyond 105 m2 s-2, outperforming most low-density materials. Extraordinary stiffness ensures their use as robust scaffolds for scaled photocatalysis and recycling without damage at the macroscopic level. A challenging but desirable reaction for direct deaminative borylation is demonstrated using CMP aerogel-based quasi-homogeneous photocatalysis with gram-scale productivity and record-high efficiency under ambient conditions. Combined terahertz and transient absorption spectroscopic studies unveil the generation of high-mobility free carriers and long-lived excitonic species in the CMP aerogels, underlying the observed superior catalytic performance.

Selenobacteria-mediated Se transformation and uptake involving the unique genetic code.

Selenium (Se) is a crucial micronutrient for human health. Plants are the primary source of Se for humans. Selenium in the soil serves as the primary source of Se for plants. The soil contains high total Se content in large areas in Guangxi, China. However, the available Se is low, hindering Se uptake by plants. Microorganisms play a pivotal role in the activation of Se in the soil, thereby enhancing its uptake by plants. In this study, selenobacteria were isolated from Se-rich soils in Guangxi. Then two selenobacteria strains, YLB1-6 and YLB2-1, representing the highest (30,000 µg/mL) and lowest (10,000 µg/mL) Se tolerance levels among the Se-tolerant bacteria, were selected for subsequent analysis. Although the two selenobacteria exhibited distinct effects, they can significantly transform Se species, resulting in a decrease in the soil residual Se (RES-Se) content while concurrently increasing the available Se (AVA-Se) content. Selenobacteria also enhance the transformation of Se valencies, with a significant increase observed in soluble Se6+ (SOL-Se6+). Additionally, selenobacteria can elevate the pH of acidic soil. Selenobacteria also promote the uptake of Se into plants. After treatment with YLB1-6 and YLB2-1, the Se content in the aboveground part of Chinese flowering cabbage increased by 1.96 times and 1.77 times, respectively, while the Se accumulation in the aboveground part of the plant significantly increased by 104.36% and 81.69%, respectively, compared to the control. Further whole-genome sequencing revealed the genetic difference between the two selenobacteria. Additionally, 46 and 38 candidate genes related to selenium utilization were identified from YLB1-6 and YLB2-1, respectively. This work accelerates our understanding of the potential molecular mechanism of Se biofortification by selenobacteria. It also provides microorganisms and gene targets for improving crop varieties or microorganisms to exploit the rich Se source in soil.

Category Alignment Mechanism for Few-Shot Image Classification.

While humans can excel at image classification tasks by comparing a few images, existing metric-based few-shot classification methods are still not well adapted to novel tasks. Performance declines rapidly when encountering new patterns, as feature embeddings cannot effectively encode discriminative properties. Moreover, existing matching methods inadequately utilize support set samples, focusing only on comparing query samples to category prototypes without exploiting contrastive relationships across categories for discriminative features. In this work, we propose a method where query samples select their most category-representative features for matching, making feature embeddings adaptable and category-related. We introduce a category alignment mechanism (CAM) to align query image features with different categories. CAM ensures features chosen for matching are distinct and strongly correlated to intra-and inter-contrastive relationships within categories, making extracted features highly related to their respective categories. CAM is parameter-free, requires no extra training to adapt to new tasks, and adjusts features for matching when task categories change. We also implement a cross-validation-based feature selection technique for support samples, generating more discriminative category prototypes. We implement two versions of inductive and transductive inference and conduct extensive experiments on six datasets to demonstrate the effectiveness of our algorithm. The results indicate that our method consistently yields performance improvements on benchmark tasks and surpasses the current state-of-the-art methods.

Shape-Dependent Optical Waveguides and Low-Threshold Lasers from Polymorphic Two-Dimensional Organic Single Crystals.

Organic single crystals (OSCs) with uniform morphologies and highly ordered molecular aggregations are promising for high-performance optoelectronic devices, such as organic solid-state lasers (OSSLs), organic light-emitting transistors (OLETs), and organic light-emitting diodes (OLEDs). However, manipulating OSC morphologies and aggregation is challenging. In this study, we synthesized two-dimensional (2D) OSCs of 4,4'-bis[(N-carbazole)styryl]biphenyl (BSBCz) in hexagonal and parallelogram microplate (H-MP and P-MP) forms. Both types exhibit H-aggregation in the 2D plate plane but with different molecular transition dipole moment (TDM) orientations. This leads to different photon coupling modes with H-MP and P-MP microcavities. H-MPs enable isotropic 2D-waveguiding, forming whispering gallery mode (WGM) resonators, while P-MPs create unidirectional waveguiding, forming Fabry-Pérot mode (FP) resonators. These resonators can generate low-threshold laser emissions at 467 and 473 nm, respectively, and exhibit superior lasing stability with a half-life exceeding 2 h. Our BSBCz microplate OSCs are attractive candidates to combine controlled organic microcavities with photon transporting for realizing future integrated optoelectronic devices.

Dual-Stimuli-Responsive Modulation Organic Afterglow Based on N─H Proton Migration Mechanism.

Organic afterglow materials have significant applications in information security and flexible electronic devices with unique optical properties. It is vital but challenging to develop organic afterglow materials possessing controlled output with multi-stimuli-responsive capacity. Herein, dimethyl terephthalate (DTT) is introduced as a strong proton acceptor. The migration direction of N─H protons on two compounds Hs can be regulated by altering the excitation wavelength (Ex) or amine stimulation, thereby achieving dual-stimuli-responsive afterglow emission. When the Ex is below 300 nm, protons migrate to S1-2 DTT, where strong interactions induce phosphorescent emission of Hs, resulting in afterglow behavior. Conversely, when the Ex is above 300 nm, protons interact with the S0 DTT weakly and the afterglow disappears. In view of amine-based compounds with higher proton accepting capabilities, it can snatch proton from S1-2 DTT and redirect the proton flow toward amine, effectively suppressing the afterglow but obtaining a new redshifted fluorescence emission with Δλ over 200 nm due to the high polarity of amine. Moreover, it is successfully demonstrated that the applications of dual-stimuli-responsive organic afterglow materials in information encryption based on the systematic excitation-wavelength-dependent (Ex-De) behavior and amine selectivity detection.

Variation in occupational exposure risk for COVID-19 workers' compensation claims across pandemic waves in Ontario.

OBJECTIVES: To understand rates of work-related COVID-19 (WR-C19) infection by occupational exposures across waves of the COVID-19 pandemic in Ontario, Canada. METHODS: We combined workers' compensation claims for COVID-19 with data from Statistics Canada's Labour Force Survey, to estimate rates of WR-C19 among workers spending the majority of their working time at the workplace between 1 April 2020 and 30 April 2022. Occupational exposures, imputed using a job exposure matrix, were whether the occupation was public facing, proximity to others at work, location of work and a summary measure of low, medium and high occupational exposure. Negative binomial regression models examined the relationship between occupational exposures and risk of WR-C19, adjusting for covariates. RESULTS: Trends in rates of WR-C19 differed from overall COVID-19 cases among the working-aged population. All occupational exposures were associated with increased risk of WR-C19, with risk ratios for medium and high summary exposures being 1.30 (95% CI 1.09 to 1.55) and 2.46 (95% CI 2.10 to 2.88), respectively, in fully adjusted models. The magnitude of associations between occupational exposures and risk of WR-C19 differed across waves of the pandemic, being weakest for most exposures in period March 2021 to June 2021, and highest at the start of the pandemic and during the Omicron wave (December 2021 to April 2022). CONCLUSIONS: Occupational exposures were consistently associated with increased risk of WR-C19, although the magnitude of this relationship differed across pandemic waves in Ontario. Preparation for future pandemics should consider more accurate reporting of WR-C19 infections and the potential dynamic nature of occupational exposures.

CORE: CORrelation-Guided Feature Enhancement for Few-Shot Image Classification.

Few-shot classification aims to adapt classifiers trained on base classes to novel classes with a few shots. However, the limited amount of training data is often inadequate to represent the intraclass variations in novel classes. This can result in biased estimation of the feature distribution, which in turn results in inaccurate decision boundaries, especially when the support data are outliers. To address this issue, we propose a feature enhancement method called CORrelation-guided feature Enrichment that generates improved features for novel classes using weak supervision from the base classes. The proposed CORrelation-guided feature Enhancement (CORE) method utilizes an autoencoder (AE) architecture but incorporates classification information into its latent space. This design allows the CORE to generate more discriminative features while discarding irrelevant content information. After being trained on base classes, CORE's generative ability can be transferred to novel classes that are similar to those in the base classes. By using these generative features, we can reduce the estimation bias of the class distribution, which makes few-shot learning (FSL) less sensitive to the selection of support data. Our method is generic and flexible and can be used with any feature extractor and classifier. It can be easily integrated into existing FSL approaches. Experiments with different backbones and classifiers show that our proposed method consistently outperforms existing methods on various widely used benchmarks.

Switching from Thermally Activated Delayed Fluorescence in Single Crystals for Low-Threshold Laser to Room-temperature Phosphorescence in Amorphous-Film for Highly Efficient OLEDs.

Metal-organic phosphorescent complexes containing Ir or Pt are work horse in organic light-emitting diode (OLED) technology, which can harvest both singlet and triplet excitons in electroluminescence (EL) owing to strong heavy-atom effect. Recently, organic room-temperature phosphorescence (ORTP) have achieved high photoluminescence quantum yield (PLQY) in rigid crystalline state, which, however, is unsuitable for OLED fabrication, therefore leading to an EL efficiency far low behind those of metal-organic phosphorescent complexes. Here, we reported a luminescence mechanism switch from thermally activated delayed fluorescence (TADF) in single crystal microwires to ORTP in amorphous thin-films, based on a tert-butylcarbazole difluoroboron ß-diketonate derivative of DtCzBF2. Tightly packed and well-faceted single-crystal microwires exhibit aggregation induced emission (AIE), enabling TADF microlasers at 473 nm with an optical gain coefficient as high as 852 cm-1 . In contrast, loosely packed dimers of DtCzBF2 formed in guest-host amorphous thin-films decrease the oscillator strength of fluorescence transition but stabilize triplets for ORTP with a PLQY up to 61 %, leading to solution-processed OLEDs with EQE approaching 20 %. This study opens possibilities of low-cost ORTP emitters for high performance OLEDs and future low-threshold electrically injected organic semiconductor lasers (OSLs).

Photochemical Reaction Enabling the Engineering of Photonic Spin-Orbit Coupling in Organic-Crystal Optical Microcavities.

The control and active manipulation of spin-orbit coupling (SOC) in photonic systems are fundamental in the development of modern spin optics and topological photonic devices. Here, we demonstrate the control of an artificial Rashba-Dresselhaus (RD) SOC mediated by photochemical reactions in a microcavity filled with an organic single crystal of photochromic phase-change character. Splitting of the circular polarization components of the optical modes induced by photonic RD SOC is observed experimentally in momentum space. By applying an ultraviolet light beam, we control the spatial molecular orientation through a photochemical reaction, and with that we control the energies of the photonic modes. This way, we realize a reversible conversion of spin splitting of the optical modes with different energies, leading to an optically controlled switching between circularly and linearly polarized optical modes in our device. Our strategy of in situ and reversible engineering of SOC induced by a light field provides a promising approach to actively design and manipulate synthetic gauge fields toward future on-chip integration in photonics and topological photonic devices.

Cotton Fibers with a Lactic Acid-Like Surface for Re-establishment of Protective Lactobacillus Microbiota by Selectively Inhibiting Vaginal Pathogens.

Failure to reconstruct the Lactobacillus microbiota is the major reason for the recurrence of vaginal infection. However, most empiric therapies focus on the efficacy of pathogen elimination but do not sufficiently consider the viability of Lactobacillus. Herein, cotton fibers with a lactic acid-like surface (LC) are fabricated by NaIO4 oxidation and L-isoserine grafting. The lactic acid analog chain ends and imine structure of LC can penetrate cell walls to cause protein cleavage in Escherichia coli and Candida albicans and inhibit vaginal pathogens. Meanwhile, the viability of Lactobacillus acidophilus is unaffected by the LC treatment, thus revealing a selective way to suppress pathogens as well as provide a positive route to re-establish protective microbiota in the vaginal tract. Moreover, LC has excellent properties such as good biosafety, antiadhesion, water absorption, and weight retention. The strategy proposed here not only is practical, but also provides insights into the treatment of vaginal infections.

Diffusion-driven fabrication of calcium and phosphorous-doped zinc oxide heterostructures on titanium to achieve dual functions of osteogenesis and preventing bacterial infections.

Conventional Ti-based implants are vulnerable to postsurgical infection and improving the antibacterial efficiency without compromising the osteogenic ability is one of the key issues in bone implant design. Although zinc oxide (ZnO) nanorods grown on Ti substrates hydrothermally can improve the antibacterial properties, but cannot meet the stringent requirements of bone implants, as rapid degradation of ZnO and uncontrolled leaching of Zn2+ are detrimental to peri-implant cells and tissues. To solve these problems, a lattice-damage-free method is adopted to modify the ZnO nanorods with thin calcium phosphate (CaP) shells. The Ca and P ions from the CaP shells diffuse thermally into the ZnO lattice to prevent the ZnO nanorods from rapid degradation and ensure the sustained release of Zn2+ ions as well. Furthermore, the designed heterostructural nanorods not only induce the osteogenic performances of MC3T3-E1 cells but also exhibit excellent antibacterial ability against S. aureus and E. coli bacteria via physical penetration. In vivo studies also reveal that hybrid Ti-ZnO@CaP5 can not only eradicates bacteria in contact, but also provides sufficient biocompatibility without causing excessive inflammation response. Our study provides insights into the design of multifunctional biomaterials for bone implants. STATEMENT OF SIGNIFICANCE: • A lattice-damage-free method is adopted to modify the ZnO nanorods with thin calcium phosphate (CaP) shells. • The dynamic process of Ca and P diffusion into the ZnO lattice is analyzed by experimental verification and theoretical calculation. • The degradation rate of ZnO nanorods is significantly decreased after CaP deposition. • The ZnO nanorods after CaP deposition can not only sterilize bacteria in contact via physical penetration, but also provide sufficient biocompatibility and osteogenic capability without causing excessive inflammation response..

Capturing Protein-Protein Interactions with Acidic Amino Acids Reactive Cross-Linkers.

Acidic residues (Asp and Glu) have a high prevalence on protein surfaces, but cross-linking reactions targeting these residues are limited. Existing methods either require high-concentration coupling reagents or have low structural compatibility. Here a previously reported "plant-and-cast" strategy is extended to develop heterobifunctional cross-linkers. These cross-linkers first react rapidly with Lys sidechains and then react with Asp and Glu sidechains, in a proximity-enhanced fashion. The cross-linking reaction proceeds at neutral pH and room temperature without coupling reagents. The efficiency and robustness of cross-linking using model proteins, ranging from small monomeric proteins to large protein complexes are demonstrated. Importantly, it is shown that this type of cross-linkers are efficient at identifying protein-protein interactions involving acidic domains. The Cross-linking mass spectrometry (XL-MS) study with p53 identified 87 putative binders of the C-terminal domain of p53. Among them, SARNP, ZRAB2, and WBP11 are shown to regulate the expression and alternative splicing of p53 target genes. Thus, these carboxylate-reactive cross-linkers will further expand the power of XL-MS in the analysis of protein structures and protein-protein interactions.

MoS2-Based Memristor: Robust Resistive Switching Behavior and Reliable Biological Synapse Emulation.

Memristors are recognized as crucial devices for future nonvolatile memory and artificial intelligence. Due to their typical neuron-synapse-like metal-insulator-metal(MIM) sandwich structure, they are widely used to simulate biological synapses and have great potential in advancing biological synapse simulation. However, the high switch voltage and inferior stability of the memristor restrict the broader application to the emulation of the biological synapse. In this study, we report a vertically structured memristor based on few-layer MoS2. The device shows a lower switching voltage below 0.6 V, with a high ON/OFF current ratio of 104, good stability of more than 180 cycles, and a long retention time exceeding 3 × 103 s. In addition, the device has successfully simulated various biological synaptic functions, including potential/depression propagation, paired-pulse facilitation (PPF), and long-term potentiation/long-term depression (LTP/LTD) modulation. These results have significant implications for the design of a two-dimensional transition-metal dichalcogenides composite material memristor that aim to mimic biological synapses, representing promising avenues for the development of advanced neuromorphic computing systems.

MG-SIN: Multigraph Sparse Interaction Network for Multitask Stance Detection.

Stance detection on social media aims to identify if an individual is in support of or against a specific target. Most existing stance detection approaches primarily rely on modeling the contextual semantic information in sentences and neglect to explore the pragmatics dependency information of words, thus degrading performance. Although several single-task learning methods have been proposed to capture richer semantic representation information, they still suffer from semantic sparsity problems caused by short texts on social media. This article proposes a novel multigraph sparse interaction network (MG-SIN) by using multitask learning (MTL) to identify the stances and classify the sentiment polarities of tweets simultaneously. Our basic idea is to explore the pragmatics dependency relationship between tasks at the word level by constructing two types of heterogeneous graphs, including task-specific and task-related graphs (tr-graphs), to boost the learning of task-specific representations. A graph-aware module is proposed to adaptively facilitate information sharing between tasks via a novel sparse interaction mechanism among heterogeneous graphs. Through experiments on two real-world datasets, compared with the state-of-the-art baselines, the extensive results exhibit that MG-SIN achieves competitive improvements of up to 2.1% and 2.42% for the stance detection task, and 5.26% and 3.93% for the sentiment analysis task, respectively.

SOX9 promotes the invasion and migration of lung adenocarcinoma cells by activating the RAP1 signaling pathway.

OBJECTIVE: SOX9 has been shown to be related to the metastasis of various cancers. Recently, it has been reported that SOX9 plays a regulatory role in lung adenocarcinoma (LUAD) cell metastasis, but the specific mechanism remains to be explored. Therefore, the objective of this study was to observe the effect and mechanism of SOX9 on the invasion and migration of LUAD cells. METHODS: RT-qPCR was applied to observe the expression of SOX9 and RAP1 in tumor tissues and corresponding normal lung tissues collected from LUAD patients. Co-immunoprecipitation and Pearson correlation to analyze the expression correlation of SOX9 with RAP1. To observe the role of SOX9, the invasion and migration levels of LUAD A549 cells in each group were observed by Transwell invasion assay and Scratch migration assay after knocking down or overexpressing SOX9. Besides, the expression levels of RAP1 pathway-related proteins (RAP1, RAP1GAP and RasGRP33) were observed by RT-qCPR or western blot. Subsequently, RAP1 was overexpressed and SOX9 was knocked down in A549 cells, and then the cell invasion/migration level and RAP1 pathway activity were assessed. RESULTS: The expression levels of SOX9 and RAP1 in tumor tissues and A549 cells of LUAD patients were significantly increased and positively correlated. Overexpression of SOX9 or RAP1 alone in A549 cells enhanced the invasion and migration ability of cells, as well as up-regulated the expression levels of RAP1, RAP1GAP and RasGRP33. However, knocking down SOX9 decreased cell invasion and migration levels and weakened the activity of RAP1 pathway. Notably, overexpressing RAP1 while knocking down SOX9 significantly activated RAP1 pathway and promoted cell invasion and migration. CONCLUSION: Overexpression of SOX9 in LUAD can significantly activate the RAP1 signaling pathway and promote cell invasion and migration.

Efficient photocatalytic production of hydrogen peroxide using dispersible and photoactive porous polymers.

Developing efficient artificial photocatalysts for the biomimetic photocatalytic production of molecular materials, including medicines and clean energy carriers, remains a fundamentally and technologically essential challenge. Hydrogen peroxide is widely used in chemical synthesis, medical disinfection, and clean energy. However, the current industrial production, predominantly by anthraquinone oxidation, suffers from hefty energy penalties and toxic byproducts. Herein, we report the efficient photocatalytic production of hydrogen peroxide by protonation-induced dispersible porous polymers with good charge-carrier transport properties. Significant photocatalytic hydrogen peroxide generation occurs under ambient conditions at an unprecedented rate of 23.7 mmol g-1 h-1 and an apparent quantum efficiency of 11.3% at 450 nm. Combined simulations and spectroscopies indicate that sub-picosecond ultrafast electron "localization" from both free carriers and exciton states at the catalytic reaction centers underlie the remarkable photocatalytic performance of the dispersible porous polymers.

Improving Embedding Generalization in Few-Shot Learning With Instance Neighbor Constraints.

Recently, metric-based meta-learning methods have been effectively applied to few-shot image classification. These methods classify images based on the relationship between samples in an embedding space, avoiding over-fitting that can occur when training classifiers with limited samples. However, finding an embedding space with good generalization properties remains a challenge. Our work highlights that having an initial manifold space that preserves sample neighbor relationships can prevent the metric model from reaching a suboptimal solution. We propose a feature learning method that leverages Instance Neighbor Constraints (INC). This theory is thoroughly evaluated and analyzed through experiments, demonstrating its effectiveness in improving the efficiency of learning and the overall performance of the model. We further integrate the INC into an alternate optimization training framework (AOT) that leverages both batch learning and episode learning to better optimize the metric-based model. We conduct extensive experiments on 5-way 1-shot and 5-way 5-shot settings on four popular few-shot image benchmarks: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), and Caltech-UCSD Birds-200-2011(CUB). Results show that our method achieves consistent performance gains on benchmarks and state-of-the-art performance. Our findings suggest that initializing the embedding space appropriately and leveraging both batch and episode learning can significantly improve few-shot learning performance.

Unpaired Multi-View Graph Clustering With Cross-View Structure Matching.

Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are predefined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred to as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) existing methods for partially unpaired problems rely on pregiven cross-view alignment information, resulting in their inability to handle fully unpaired problems; and 3) their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed unpaired multi-view graph clustering framework with cross-view structure matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.

Highly efficient removal of rare earth elements by two-dimensional titanium carbide nanosheets as impacted via water chemistry.

The separation and recycling of rare earth elements (REEs) are very important owing to the high demand, limited resource, specific usages, and environmental issues. In this work, two-dimensional Ti3C2Tx MXene was introduced to remove REEs (Nd(III) and La(III)) from water, and its physicochemical properties were conducted by HRTEM, SEM-EDS, XRD, FTIR, and XPS. Various parameters, such as initial pH, REEs initial concentration, contact time, and temperature, were investigated by batch experiment, respectively. Furthermore, the adsorption kinetic and isotherm were examined to analyze the adsorption behavior and adsorption mechanism. Nd(III) and La(III) have a good affinity with Ti3C2Tx MXene surface functional groups (-F, -OH, and containing oxygen groups). The maximum adsorption capacities of Ti3C2Tx MXene for Nd(III) and La(III) were 229.85 mg/g and 175.83 mg/g at T = 333 K, respectively. The adsorption data of Nd(III) on Ti3C2Tx MXene fitted well with the Freundlich isotherms model and pseudo-second-order kinetic model. However, the best fitting for La(III) adsorption on Ti3C2Tx MXene was described by both pseudo-first-order and pseudo-second-order model. Thermodynamic study of Nd(III) and La(III) adsorption on Ti3C2Tx MXene showed that the reaction was a spontaneous and endothermic process. These results indicated Ti3C2Tx MXene had a great potential in extracting REEs from an aqueous solution.

Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses.

Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D'-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D') into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu-net that accommodates nanoscale [α-SiW12 O40 ]4- counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.