Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems.

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.

Semiconductor Quantum Dots for Memories and Neuromorphic Computing Systems.

The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin-film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.

Near-Infrared Artificial Synapses for Artificial Sensory Neuron System.

The computing based on artificial neuron network is expected to break through the von Neumann bottleneck of traditional computer, and to greatly improve the computing efficiency, displaying a broad prospect in the application of artificial visual system. In the specific structural layout, it is a common method to connect the discrete photodetector with the artificial neuron in series, which enhances the complexity of signal recognition, conversion and storage. In this work, organic small molecule IR-780 iodide is inserted into the memory device as both the charge trapping layer and near-infrared (NIR) photoresponsive film. Through electrical and optical regulation, artificial synaptic functions including short-term plasticity, long-term plasticity, and spike rate dependence are realized. In the established artificial sensory neuron system, NIR optical pulses can significantly improve the spiking rate. Moreover, the spiking neural networks are further constructed by simulation for handwritten digit classification. This research may contribute to the development of light driven neural robots, optical signal encryption, and neural computing.

Chirality in peptide-based materials: From chirality effects to potential applications.

Chirality is ubiquitous in nature with primary cellular functions that include construction of right-/left-handed helix and selective communications among diverse biomolecules. Of particularly intriguing are the chiral peptide-based materials that can be deliberately designed to change physicochemistry properties via tuning peptide sequences. Critically, understanding their chiral effects are fundamental for the development of novel materials in chemistry and biomedicine fields. Here, we review recent researches on chirality in peptide-based materials, summarizing relevant typical chiral effects towards recognition, amplification, and induction. Driven forces for the chiral discrimination in affinity interaction as well as the handedness preferences in supramolecular structure formation at both the macroscale and microscale are illustrated. The implementation of such chirality effects of artificial copolymers, assembled aggregates and their composites in the fields of bioseparation and bioenrichment, cell incubation, protein aggregation inhibitors, chiral smart gels, and bionic electro devices are also presented. At last, the challenges in these areas and possible directions are pointed out. The diversity of chiral roles in the origin of life and chirality design in different organic or composite systems as well as their applications in drug development and chirality detection in environmental protection are discussed.

Building memory devices from biocomposite electronic materials.

Natural biomaterials are potential candidates for the next generation of green electronics due to their biocompatibility and biodegradability. On the other hand, the application of biocomposite systems in information storage, photoelectrochemical sensing, and biomedicine has further promoted the progress of environmentally benign bioelectronics. Here, we mainly review recent progress in the development of biocomposites in data storage, focusing on the application of biocomposites in resistive random-access memory (RRAM) and field effect transistors (FET) with their device structure, working mechanism, flexibility, transient characteristics. Specifically, we discuss the application of biocomposite-based non-volatile memories for simulating biological synapse. Finally, the application prospect and development potential of biocomposites are presented.

Near-Infrared Annihilation of Conductive Filaments in Quasiplane MoSe2 /Bi2 Se3 Nanosheets for Mimicking Heterosynaptic Plasticity.

It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre- and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high-performance artificial heterosynaptic systems. Nevertheless, the utilization of near-infrared (NIR) irradiation to act as a modulatory terminal for heterosynaptic plasticity emulation has not yet been realized. Here, an NIR resistive random access memory (RRAM) is reported, based on quasiplane MoSe2 /Bi2 Se3 heterostructure in which the anomalous NIR threshold switching and NIR reset operation are realized. Furthermore, it is shown that such an NIR irradiation can be employed as a modulatory terminal to emulate heterosynaptic plasticity. The reconfigurable 2D image recognition is also demonstrated by an RRAM crossbar array. NIR annihilation effect in quasiplane MoSe2 /Bi2 Se3 nanosheets may open a path toward optical-modulated in-memory computing and artificial retinal prostheses.

Biological Spiking Synapse Constructed from Solution Processed Bimetal Core-Shell Nanoparticle Based Composites.

Inspired by the highly parallel processing power and low energy consumption of the biological nervous system, the development of a neuromorphic computing paradigm to mimic brain-like behaviors with electronic components based artificial synapses may play key roles to eliminate the von Neumann bottleneck. Random resistive access memory (RRAM) is suitable for artificial synapse due to its tunable bidirectional switching behavior. In this work, a biological spiking synapse is developed with solution processed Au@Ag core-shell nanoparticle (NP)-based RRAM. The device shows highly controllable bistable resistive switching behavior due to the favorable Ag ions migration and filament formation in the composite film, and the good charge trapping and transport property of Au@Ag NPs. Moreover, comprehensive synaptic functions of biosynapse including paired-pulse depression, paired-pulse facilitation, post-tetanic potentiation, spike-time-dependent plasticity, and the transformation from short-term plasticity to long-term plasticity are emulated. This work demonstrates that the solution processed bimetal core-shell nanoparticle-based biological spiking synapse provides great potential for the further creation of a neuromorphic computing system.

Chiral effect at protein/graphene interface: a bioinspired perspective to understand amyloid formation.

Protein misfolding to form amyloid aggregates is the main cause of neurodegenerative diseases. While it has been widely acknowledged that amyloid formation in vivo is highly associated with molecular surfaces, particularly biological membranes, how their intrinsic features, for example, chirality, influence this process still remains unclear. Here we use cysteine enantiomer modified graphene oxide (GO) as a model to show that surface chirality strongly influences this process. We report that R-cysteine modification suppresses the adsorption, nucleation, and fiber elongation processes of Aß(1-40) and thus largely inhibits amyloid fibril formation on the surface, while S-modification promotes these processes. And surface chirality also greatly influences the conformational transition of Aß(1-40) from α-helix to ß-sheet. More interestingly, we find that this effect is highly related to the distance between chiral moieties and GO surface, and inserting a spacer group of about 1-2 nm between them prevents the adsorption of Aß(1-40) oligomers, which eliminates the chiral effect. Detailed study stresses the crucial roles of GO surface. It brings novel insights for better understanding the amyloidosis process on surface from a biomimetic perspective.

Solvent-driven chiral-interaction reversion for organogel formation.

For chiral gels and related applications, one of the critical issues is how to modulate the stereoselective interaction between the gel and the chiral guest precisely, as well as how to translate this information into the macroscopic properties of materials. Herein, we report that this process can also be modulated by nonchiral solvents, which can induce a chiral-interaction reversion for organogel formation. This process could be observed through the clear difference in gelation speed and the morphology of the resulting self-assembly. This chiral effect was successfully applied in the selective separation of quinine enantiomers and imparts "smart" merits to the gel materials.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors.

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.

View-Consistency Learning for Incomplete Multiview Clustering.

In this article, we present a novel general framework for incomplete multi-view clustering by integrating graph learning and spectral clustering. In our model, a tensor low-rank constraint are introduced to learn a stable low-dimensional representation, which encodes the complementary information and takes into account the cluster structure between different views. A corresponding algorithm associated with augmented Lagrangian multipliers is established. In particular, tensor Schatten p -norm is used as a tighter approximation to the tensor rank function. Besides, both consistency and specificity are jointly exploited for subspace representation learning. Extensive experiments on benchmark datasets demonstrate that our model outperforms several baseline methods in incomplete multi-view clustering.

Laser Irradiation Effect on the p-GaSe/n-HfS2 PN-Heterojunction for High-Performance Phototransistors.

Two-dimensional (2D)-based PN-heterojunction revealed a promising future of atomically thin optoelectronics with diverse functionalities in different environments. Herein, we reported a p-GaSe/n-HfS2 van der Waals (vdW) heterostructure for high-performance photodetectors and investigated the laser irradiation effect on the fabricated device. The fabricated 2D vdW heterostructure revealed a high photoresponsivity of 1 × 104 A W-1 with a photocurrent value of 377 nA due to unique type-II band alignment and enhanced surface potential under light illumination, which is further confirmed by density functional theory (DFT) calculations. Before laser irradiation, the device showed high field-effect mobility (µEF) of 26.37 cm2 V-1 s-1, ON/OFF ratio of ∼105, and threshold voltage swing (SS) of ∼463 mV dec-1. With the exposure of 690 mW cm-2 laser power density, µEF reached 204 cm2 V-1 s-1, although ∼2 V ΔVth shifts are observed along with the SS decreased to 175 mV dec-1. Interestingly, the reduced SS shows better channel control of the fabricated device with laser power. Similarly, the ON/OFF ratio decreased to ∼1.29 × 103. The results indicate that the creation of oxide trap charges at the interface of SiO2 and PN-heterojunction layers was observed with voltage biasing and high laser power density. The degradation of electrical parameters is attributed to fewer interface trap charges per surface area of the device rather than direct damage in PN-heterojunction layers. Considering the excellent 2D electronic properties, these materials are better candidates for future high-radiation environments.

Memristor-based biomimetic compound eye for real-time collision detection.

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

Rabies Virus-Induced Autophagy Is Dependent on Viral Load in BV2 Cells.

An increasing number of studies are showing that autophagy plays a vital role in viral replication and escape. Rabies virus (RABV), a typical neurotropic virus, has been proven to induce autophagy in neurons. However, there are no reports indicating that RABV can cause autophagy in other cells of the central nervous system. Thus, we aimed to explore the relationship between autophagy and RABV infection in BV2 cells in this study. Results of viral growth curves showed that the titers of microglial BV2 cells infected with RABV peaked at 12 hours post-infection (hpi) and then decreased continuously over time. However, it was found that the viral genome RNA and structural proteins can express normally in BV2 cells. In addition, Western blotting indicated that RABV infection increased LC3-II and p62 expression in BV2 cells. LC3 punctate increased with RABV infection in BV2 cells after the transfection of fluorescent protein-tagged LC3 plasmids. Moreover, autophagy cargo protein further accumulated with RABV infection in Bafilomycin A1-treated cells. Subsequently, RABV infection inhibited the fusion of autophagosomes with lysosomes by using a tandem fluorescent marker. Furthermore, a higher multiplicity of infection induced stronger autophagy. Thus, RABV can induce autophagy in BV2 cells, and the autophagy is positively associated with the viral load.

A methylation-inspired mesoporous coordination polymer for identification and removal of organic pollutants in aqueous solutions.

Qualitative analysis of contamination events and rapid removal of hazardous substances from water are in urgent need for a sustainable environment and human health. Porous coordination polymers (PCPs) bridged by organic ligands through metal nodes in an extendable and periodic manner have emerged as competitive candidates for the detection and removal of hazardous substances. However, the majority of them suffer from high production costs, poor structural stability and environmental problems, which has become a bottleneck for commercial translation. Here, we report a class of phenylalanine-based metal-biomolecule complexes and discuss the impact of subtle sequence variations in modular ligands on their assembly behaviors and structural properties. The phenomenon in which the bioligands dominate the structure formation and surface wettability has been revealed. A Cu(ii)-aspartame coordination polymer, Cu(mDF), with satisfactory chemical stability was selected for removal of organic pollutants in aqueous solution. The mesoporous structure, surface charge and high specific surface area (233.71 m2 g-1, Dmean = 5.65 nm) promote its rapid equilibrium of anionic adsorption within 10 min. In addition, Cu(mDF) possessing an adsorption-induced color-shifting feature provides an ideal platform for organic pollutant detection. Furthermore, Cu(mDF) with biocompatibility and low cost fabrication exhibits antimicrobial properties against C. albicans, E. coli and S. aureus, and may be a potential purifier in wastewater treatment.

Recent advancements in polyethyleneimine-based materials and their biomedical, biotechnology, and biomaterial applications.

Cationic polymers, known for their highly positive charges, have historically dominated the materials used in bioengineering. However, the demand for intelligent systems with high efficiency, bio-mimetic and tunable features is increasing. Artificial composites that mimic biorecognition and periodic structures may propel the development of advanced materials with outstanding properties. Polyethyleneimines (PEIs) constitute a valuable class of polycations because they have repetitive structural units, a wide molecular weight range and flexible polymeric chains, which facilitate customization of functional composites. Specific advantageous features could be introduced by purposeful modification or functionalization, such as specificity and sensitivity, distinct geometry, biocompatibility, and long service life. Thus, PEIs have been rapidly used in a wide range of applications in the fields of biomedicine, biotechnology and biomaterials science. This article provides an overview of recent advancements in the fabrication of PEI-based materials and corresponding applications in gene and drug delivery, bio-inhibitors, bio-separation, bioimaging, cell culture, and production of antibacterial and self-healing materials. The effects of molecular weight, topological structure, positive charges and hydrophilic properties on the performance of PEIs have been illustrated in detail. Finally, current technological limitations, research challenges, and future aspects are also discussed.

Recent advances in optical and optoelectronic data storage based on luminescent nanomaterials.

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Modulation of Binary Neuroplasticity in a Heterojunction-Based Ambipolar Transistor.

To keep pace with the upcoming big-data era, the development of a device-level neuromorphic system with highly efficient computing paradigms is underway with numerous attempts. Synaptic transistors based on an all-solution processing method have received growing interest as building blocks for neuromorphic computing based on spikes. Here, we propose and experimentally demonstrated the dual operation mode in poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl}(PDPPBTT)/ZnO junction-based synaptic transistor from ambipolar charge-trapping mechanism to analog the spiking interfere with synaptic plasticity. The heterojunction formed by PDPPBTT and ZnO layers serves as the basis for hole-enhancement and electron-enhancement modes of the synaptic transistor. Distinctive synaptic responses of paired-pulse facilitation (PPF) and paired-pulse depression (PPD) were configured to achieve the training/recognition function for digit image patterns at the device-to-system level. The experimental results indicate the potential application of the ambipolar transistor in future neuromorphic intelligent systems.

Circularly polarized light modulated supramolecular self-assembly for an azobenzene-based chiral gel.

UV light-triggered trans-to-cis isomerization of azobenzene usually results in the collapse of a self-assembly system owing to the breaking of molecular planarity. Interestingly, two totally opposite self-assembly trends have been detected when a C 2v-symmetric chiral gelator was irradiated by a circularly polarized light (CPL) with specific handedness, indicating that CPL could become a powerful tool in modulating the assembly behaviour of the photo-responsive system.