Type I photodynamic therapy is considered to be a more promising cancer treatment than type II photodynamic therapy due to its non-oxygen-dependent characteristics. In this work, three D-A structure N,N'-dihydrophenazine (DHP)-based photosensitizers DP-CNPY, SMP-CNPY and DMP-CNPY were designed and synthesized by introducing different numbers of methyl groups in the backbone neighbor of DHP as the donor and combined with the typical strong electron acceptor 2-(pyridin-4-yl)acetonitrile. Among the three photosensitizers, SMP-CNPY with one methyl modification showed the best type I ROS (O2-Ë, ËOH) generation capacity and AIE performance. By encapsulation, SMP-CNPY was fabricated into nanoparticles, and SMP-CNPY NPs exhibited lipid droplet targeting ability with near-infrared (NIR) emission. Cell experiments have proved that SMP-CNPY NPs can effectively kill different kinds of cancer cells under normal oxygen conditions. Even under hypoxic and extreme hypoxic conditions, SMP-CNPY NPs can still produce ROS and kill cancer cells. This work holds significant potential in the field of type I AIE-active photosensitizers and provides a new strategy for overcoming the hypoxic dilemma in the malignant tumor microenvironment.
Introduction: The Sanxingdui Site in Guanghan City, Sichuan Province, China, is one of the precious heritage sites of the ancient Chinese civilization. Archaeological work at Sanxingdui is of great significance in clarifying the origins and main contents of the ancient Shu culture and the Yangtze River civilization. Since the 1920s, archaeologists have conducted extensive excavations and research at the site, with particular attention given to the large number of ivory artifacts unearthed. However, the buried ivory is influenced by soil pH, temperature, humidity, and other physical and chemical factors, along with the potential impact of microbial activities that may lead to the corrosion and decomposition of ivory. By understanding the types and activities of microorganisms, appropriate measures can be taken to protect and preserve cultural relics. Methods: Multi-point sampling of soil samples around the ivory of the three sacrificial pits at the Sanxingdui site was carried out, and strict aseptic operation was carried out during the sampling process. Subsequently, the microbial community structure and diversity in the buried ivory soil of Sanxingdui site were identified and analyzed by Illumina high-throughput sequencing technology. Results: 16S rRNA and internal transcribed spacer sequence analysis revealed significant differences in the soil microbial community structure among different sacrificial pits. The dominant bacterial phyla were the Proteobacteria, GAL15, Actinobacteriota, Bacteroidota, and Methylomirabilota. The dominant fungal phyla were Ascomycota, Mortierellomhcota, and Basidiomycota. Most dominant bacterial and fungal communities play an indispensable role in the ivory corrosion mechanism, promoting the decay and decomposition process through various means such as decomposing organic matter and producing acidic substances. Discussion: It is particularly important to take a series of measures to control microbial activity to effectively protect ivory. Our preliminary study of the mechanism of action of microorganisms on ivory in a buried environment provides a scientific basis to prevent and protect against microbial degradation in ancient ivory unearthed in Sanxingdui. Following the research results, suitable antibacterial agents tailored to the preservation environment and microbial characteristics of ancient ivory can be prepared. Ensure that the selected antibacterial agents meet safety and effectiveness requirements to maximize protection against microbial degradation of ancient ivory.
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
A novel type of electron donor-acceptor system was built from a nitrogen-rich covalent organic framework (PC) and a polyoxometalate (BW12), fabricating a composite material (BW12@PC-250), which shows significantly improved photocatalytic H2O2 yield (56.4 µM h-1) under full spectrum illumination in pure water, being about 30 times higher than that of PC. This is due to the opening of the electron and proton transport pathway between PC and BW12, which paves a new way for POMs to modulate the photocatalytic reactions of COFs.
The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.
The development of all-in-one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real-time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet-of-Things (IoT) has led to the increasing importance of in-sensor computing technology, which places computational power at the edge of the data-flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two-dimensional (2D) α-In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate-tunable linearity of excitatory and inhibitory functions in photon-induced short-term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in-sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre-programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature-enhanced scenarios. The findings demonstrate the potential of ferroelectric α-In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
Critically ill patients with Corona Virus Disease 2019 (COVID-19) often develop secondary bacterial infections that pose a significant threat to patient life safety, making the development of drugs to prevent bacterial infections in the lungs critical to clinical care. Naringin (NAR) is one of the significant natural flavonoids rich in Pummelo Peel (Hua Ju Hong), with anti-inflammatory, antimicrobial, and antioxidant activities, and is commonly used in treating respiratory tract infectious diseases. In this study, the in vitro and in vivo findings revealed that, after Klebsiella pneumoniae (Kpn) infection, NAR inhibited overactivation of the nuclear factor kappa-B(NF-κB) signaling pathway in alveolar macrophages of mice, reduced neutrophil (NEs) recruitment, and lowered the induced production of proinflammatory markers, such as Interleukin-6(IL-6) and tumor necrosis factor α(TNF-α). Thus, it suppressed excessive immune responses in the lungs, as well as attenuated the induced pulmonary fibrosis and inflammatory infiltrates. These results suggest that NAR has a preventive effect against Kpn in mice. In addition, the study evaluated NAR's potential toxicity, demonstrating that NAR is safe at effective doses. These results suggested that NAR effectively reduces excessive inflammatory damage in the lungs induced by Kpn and enhances the body's ability to clear bacteria. Therefore, NAR may be an effective and safe healthcare drug for preventing and caring for bacterial pneumonia.
Klebsiella pneumoniae , Pneumonia, Bacterial , Mice , Humans , Animals , Klebsiella pneumoniae/metabolism , NF-kappa B/metabolism , Signal Transduction , Pneumonia, Bacterial/drug therapy , Tumor Necrosis Factor-alpha/metabolism
The electrocatalytic hydrogen evolution reaction (HER) is an ideal method for hydrogen production. Transition metal complex electrocatalysts exhibit poor HER activity due to excessive or weak adsorption of H during the electrochemical reduction of water to molecular hydrogen in acidic environments. Developing specific functional complex materials as desired catalysts is challenging. Here, an electrochemical surface restructuring strategy of polyoxometalate (POM)-modified Ag materials toward the HER with a dramatically decreased overpotential under acidic aqueous conditions is established. We prepared two POM [SiW12O40]4- (SiW12)/[P2W18O62]6- (P2W18)-based Ag-2,2'-biimidazole (H2biim) inorganic-organic hybrid compounds (1 and 2) via the hydrothermal method and these two compounds undergo an electrochemical restructuring process in 0.5 M H2SO4 during the HER, in which Ag nanoparticles are in situ formed with the basic structures of SiW12 and P2W18 being maintained. The activated catalysts (1-AC-RDE and 2-AC-RDE) exhibit good electrocatalytic activity for the HER with good long-term stability, and the required overpotentials at a current density of 10 mA cm-2 are 112 mV (1-AC-RDE) and 91 mV (2-AC-RDE) with Tafel slopes of 77 mV dec-1 and 65 mV dec-1, respectively. The excellent electron-proton storage and transferability of SiW12 and P2W18 may provide a solution for the insufficient capture of H by Ag, leading to an effective self-optimizing behavior and superior acidic HER activity.
BACKGROUND: N6-methyladenosine (m6A) modification is the most abundant type of RNA modification in eukaryotic cells, playing pivotal roles in multiple plant growth and development processes. Yet the potential role of m6A in conferring the trait of male sterility in plants remains unknown. RESULTS: In this study, we performed RNA-sequencing (RNA-Seq) and m6A-sequencing (m6A-Seq) of RNAs obtained from the anther tissue of two wolfberry lines: 'Ningqi No.1' (LB1) and its natural male sterile mutant 'Ningqi No.5' (LB5). Based on the newly assembled transcriptome, we established transcriptome-wide m6A maps for LB1 and LB5 at the single nucleus pollen stage. We found that the gene XLOC_021201, a homolog of m6A eraser-related gene ALKBH10 in Arabidopsis thaliana, was significantly differentially expressed between LB1 and LB5. We also identified 1642 and 563 m6A-modified genes with hypermethylated and hypomethylated patterns, respectively, in LB1 compared with LB5. We found the hypermethylated genes significantly enriched in biological processes related to energy metabolism and lipid metabolism, while hypomethylation genes were mainly linked to cell cycle process, gametophyte development, and reproductive process. Among these 2205 differentially m6A methylated genes, 13.74% (303 of 2205) were differentially expressed in LB1 vis-à-vis LB5. CONCLUSIONS: This study constructs the first m6A transcriptome map of wolfberry and establishes an association between m6A and the trait of male sterility in wolfberry.
Infertility, Male , Lycium , Male , Humans , Gene Expression Profiling , Lycium/genetics , Transcriptome , RNA , DNA Methylation/genetics , Infertility, Male/genetics
Due to its large volume and poor thermal conductivity, mass concrete is prone to temperature cracking caused by heat release during cement hydration after pouring. To address the issue of temperature cracking in mass concrete, this study utilized emulsion polymerization to prepare polybutyl acrylate (PBA) emulsions. At an optimal dosage of 1.5%, the addition of a PBA emulsion reduced the temperature rise of cement paste by 12.4%. The inhibitory mechanism of a PBA emulsion on cement hydration was analyzed by characterization techniques such as isothermal calorimetry, X-ray diffraction Rietveld full-profile fitting method (XRD), thermogravimetric-differential scanning calorimetry (TG-DSC), and mercury intrusion porosimetry (MIP). The results showed that the C3S content in the cement specimens with 1%, 1.5%, and 2% PBA increased by 13.83%, 23.52%, and 34.65% compared to the blank group, respectively, while the C3A content increased by 92.59%, 79.63%, and 96.30%, respectively. The addition of a PBA emulsion can slow down the hydration rate of C3S and C3A, thereby reducing the temperature rise and fall rate of cement hydration, reducing the peak heat release of the hydration reaction, and ultimately achieving the inhibition of the cement hydration reaction. In addition, the mechanical properties of PBA-modified cement-based materials were also tested. The results show that the addition of PBA can affect the early strength development of cement samples, but has no effect on the strength after 60 days. Therefore, PBA can be used as a hydration temperature rise control material to reduce the risk of temperature cracking in mass concrete.
Carboxylesterases (CEs) play important physiological roles in the human body and are involved in numerous cellular processes. Monitoring CEs activity has great potential for the rapid diagnosis of malignant tumors and multiple diseases. Herein, we developed a new phenazine-based "turn-on" fluorescent probe DBPpys by introducing 4-bromomethyl-phenyl acetate to DBPpy, which can selectively detect CEs with a low detection limit (9.38 × 10-5 U/mL) and a large Stokes shift (more than 250 nm) in vitro. In addition, DBPpys can also be converted into DBPpy by carboxylesterase in HeLa cells and localized in lipid droplets (LDs), emitting bright near-infrared fluorescence under the irradiation of white light. Moreover, we achieved the detection of cell health status by measuring the intensity of NIR fluorescence after co-incubation of DBPpys with H2O2-pretreated HeLa cells, indicating that DBPpys has great potential applications for assessing CEs activity and cellular health.
Carboxylic Ester Hydrolases , Fluorescent Dyes , Humans , Cell Survival , Lipid Droplets , HeLa Cells , Hydrogen Peroxide , Optical Imaging
The nonlinear parametric process is of great significance for achieving high-quality coherent optical signals and quantum correlated photons. With the development of classical and quantum information processing, the study of the properties of parametric processes is evolving in complex scenarios of multimode, which is limited in conventional nonlinear media due to strict phase matching, e.g. nonlinear crystals. Here we study the dressing-energy-level-cascaded four-wave mixing process to generate multimode optical parametric signals. Via cascading double-Λ type configuration of 85Rb D1 line, the non-degenerate energy-level-cascaded FWM is constructed to generate multimode self-parametric amplification. Moreover, with the dressing effects based on atomic coherence, the spatial and frequency multimode characteristics of energy-level-cascaded FWM parametric amplification, i.e., the modes number and pattern, are actively modulated by the pump fields detuning. Also, the spatial modes from the coupling of two coexisting spontaneous parametric FWMs can be controlled to reach tremendous scalability via the atomic coherence and Kerr non-linearity. The atomic coherence effects and unique phase-matching symmetry nature allow flexible modulation of the multimode property of the generated parametric signals within a nonlinear device, which paves a way for multimode classical and quantum information processing.
Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited for spike encoding, rendering it suitable for realizing biomimetic encoders. Accordingly, a high-performance ferroelectric encoder is achieved, featuring a superior switching efficiency, negligible charge trapping effect, and robust ferroelectric response, which successfully enable a broad dynamic range. Furthermore, an SNN is simulated to verify the precision of the encoded information, in which an average inference accuracy of 95.14% can be achieved, using the Modified National Insitute of Standards and Technology (MNIST) dataset for digit classification. Moreover, this ferroelectric encoder manifests prominent resilience against noise injection with an overall prediction accuracy of 94.73% under various Gaussian noise levels, showing practical promises to reduce the computational load for the neural network.
Artificial Intelligence , Molybdenum , Action Potentials/physiology , Neurons/physiology , Neural Networks, Computer
α-Arylated carboxylic acids, esters and amides are widespread motifs in bioactive molecules and important building blocks in chemical synthesis. Thus, straightforward and rapid access to such structures is highly desirable. Here we report an organophotocatalytic multicomponent synthesis of α-arylated carboxylic acids, esters and amides from exhaustive defluorination of α-trifluoromethyl alkenes in the presence of alkyltrifluoroborates, water and nitrogen/oxygen nucleophiles. This operationally simple strategy features a unified access to functionally diverse α-arylated carboxylic acids, esters, and primary, secondary, and tertiary amides through backbone assembly from simple starting materials enabled by consecutive C-F bond functionalization at room temperature. Preliminary mechanistic investigations reveal that the reaction operates through a radical-triggered three-step cascade process, which involves distinct mechanisms for each defluorinative functionalization of the C-F bond.
Coupling charge impurity scattering effects and charge-carrier modulation by doping can offer intriguing opportunities for atomic-level control of resistive switching (RS). Nonetheless, such effects have remained unexplored for memristive applications based on 2D materials. Here a facile approach is reported to transform an RS-inactive rhenium disulfide (ReS2 ) into an effective switching material through interfacial modulation induced by molybdenum-irradiation (Mo-i) doping. Using ReS2 as a model system, this study unveils a unique RS mechanism based on the formation/dissolution of metallic ß-ReO2 filament across the defective ReS2 interface during the set/reset process. Through simple interfacial modulation, ReS2 of various thicknesses are switchable by modulating the Mo-irradiation period. Besides, the Mo-irradiated ReS2 (Mo-ReS2 ) memristor further exhibits a bipolar non-volatile switching ratio of nearly two orders of magnitude, programmable multilevel resistance states, and long-term synaptic plasticity. Additionally, the fabricated device can achieve a high MNIST learning accuracy of 91% under a non-identical pulse train. The study's findings demonstrate the potential for modulating RS in RS-inactive 2D materials via the unique doping-induced charged impurity scattering property.
In-memory computing based on memristor arrays holds promise to address the speed and energy issues of the classical von Neumann computing system. However, the stochasticity of ions' transport in conventional oxide-based memristors imposes severe intrinsic variability, which compromises learning accuracy and hinders the implementation of neural network hardware accelerators. Here, these challenges are addressed using a low-voltage memristor array based on an ultrathin PdSeOx /PdSe2 heterostructure switching medium realized by a controllable ultraviolet (UV)-ozone treatment. A distinctively different ions' transport mechanism is revealed in the heterostructure that can confine the formation of conductive filaments, leading to a remarkable uniform switching with low set and reset voltage variability values of 4.8% and -3.6%, respectively. Moreover, convolutional image processing is further implemented using various crossbar kernels that achieve a high recognition accuracy of ≈93.4% due to the highly linear and symmetric analog weight update as well as multiple conductance states, manifesting its potential beyond von Neumann computing.
To gain insights into the coupling of conformational and electronic variables, we exploited steric hindrance to modulate a polycyclic skeleton with a bent conformation in the S0 state and a twisted conformation in the S1 state under the guidance of photoexcited aromaticity reversals. Polycyclic 5,10-dihydrophenazine (DHP) adopted a bent structure in S0 but involved a bent-to-planar transformation in S1 due to the excited-state aromaticity of the 8π-electron central ring. The N,N'-locations and 1,4,6,9-sites of the DHP skeleton provided a versatile chemical handle for fine-tuning intramolecular steric hindrance. Specifically, N,N'-diphenyl-5,10-dihydrophenazine (DPP-00) and its derivatives DPP-10-DPP-22 were synthesized with different numbers of methyl groups on the 1,4,6,9-sites. X-ray crystal analyses suggested that the DHP skeletons of DPP-00-DPP-22 had more bending configurations along the N···N axis with an increase in the number of methyl groups. Following the bending-promoted interruption of π-conjugation, the absorption spectra of DPP-00-DPP-22 significantly blue-shifted from 416 to 324 nm. By contrast, the emission bands exhibited a reverse shift to longer wavelengths from 459 to 584 nm as the number of methyl substituents increased. Theoretical calculations revealed that introducing methyl groups caused the planar DHP skeleton in S1 to further twist along the N···N axis, resulting in a twisted high-strain conformation. The greater Stokes shift of the more steric-hindered structure can be attributed to the release of larger strain and aromatic stabilization energy. This research highlighted the potential promise associated with the interplay of steric effects and aromaticity reversals in a single fluorophore.
Electrons , Fluorescent Dyes , Electronics , Molecular Conformation
Fluorine-containing molecules have found broad applications in pharmaceutical and agrochemical industries as introducing fluorine into a molecule could significantly tune the biological activities of parent molecules. Thus, the synthesis of fluorine-containing molecules has received substantial attention over the past few decades. As a complementary strategy for the synthesis of fluorinated compounds through new Csp3-F bonds formation, selective cleavage of inert Csp3-F bonds from easily-available and cost-effective multifluorinated molecules, such as fluoroalkylaromatics, α-trifluoromethyl alkenes and α-multifluorinated carbonyl compounds, has been emerging as an attractive alternative to access fluorine-containing molecules. Moreover, the inherent nature of radical reactions offers the opportunity for the selective Csp3-F functionalizations to occur under mild conditions. In this regard, the development of photoredox catalysis, transition-metal catalysis, or electrochemistry to enable radical species generation via selective Csp3-F cleavage has gained broad attention and substantial progress has been made over recent years. This highlight summerizes the recent advances in the single-electron-transfer enabled selective functionalizations of Csp3-F bonds in multifluorinated compounds via radical pathways.
Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe2 ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.
Hafnium , Neural Networks, Computer , Computers , Physical Phenomena
