Aqueous chemimemristor based on proton-permeable graphene membranes.

Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.

Amphiphilic Polyphosphazene for Fluorocarbon Emulsion Stabilization.

High internal phase emulsions (HIPEs) have been of great interest for fabricating fluorinated porous polymers having controlled pore structures and excellent physicochemical properties. However, it remains a challenge to prepare stable fluorocarbon HIPEs, due to the lack of suitable surfactants. By randomly grating hydrophilic and fluorophilic side chains to polyphosphazene (PPZ), a comb-like amphiphilic PPZ surfactant with biodegradability is designed and synthesized for stabilizing water/fluorocarbon oil-based emulsions. The hydrophilic-lipophilic balance of PPZs can be controlled by tuning the grating ratio of the two side chains, leading to the preparation of stable water-in-oil HIPEs and oil-in-water emulsions, and the production of fluorinated porous polymers and particles by polymerizing the oil phase. These fluorinated porous polymers show excellent thermal stability and, due to the hydrophobicity and porous structure, applications in the field of oil/water separation can be achieved.

Reexamination of Damping in Sliding Friction.

Friction is responsible for about one-third of the primary energy consumption in the world. So far, a thorough atomistic understanding of the frictional energy dissipation mechanisms is still lacking. The Amontons' law states that kinetic friction is independent of the sliding velocity while the Prandtl-Tomlinson model suggests that damping is proportional to the relative sliding velocity between two contacting objects. Through careful analysis of the energy dissipation process in atomic force microscopy measurements, here we propose that damping force is proportional to the tip oscillation speed induced by friction. It is shown that a physically well-founded damping term can better reproduce the multiple peaks in the velocity-dependent friction force observed in both experiments and molecular dynamics simulations. Importantly, the analysis gives a clear physical picture of the dynamics of energy dissipation in different friction phases, which provides insight into long-standing puzzles in sliding friction, such as velocity weakening and spring-stiffness-dependent friction.

Substrate effect on charging of electrified graphene/water interfaces.

Graphene, a transparent two-dimensional (2D) conductive electrode, has brought extensive new perspectives and prospects to electrochemical systems, such as chemical sensors, energy storage, and energy conversion devices. In many of these applications, graphene, supported on a substrate, is in contact with an aqueous solution. An increasing number of studies indicate that the substrate, rather than graphene, determines the organization of water in contact with graphene, i.e., the electric double layer (EDL) structure near the electrified graphene, and the wetting behavior of the graphene: the graphene sheet is transparent in terms of its supporting substrate. By applying surface-specific heterodyne-detected sum-frequency generation (HD-SFG) spectroscopy to the silicon dioxide (SiO2)-supported graphene electrode/aqueous electrolyte interface and comparing the data with those for the calcium fluoride (CaF2)-supported graphene [Y. Wang et al., Angew. Chem., Int. Ed., 2023, 62, e202216604], we discuss the impact of the different substrates on the charging of both the graphene and the substrate upon applying potentials. The SiO2-supported graphene shows pseudocapacitive behavior, consistent with the CaF2-supported graphene case, although the surface charges on SiO2 and CaF2 differ substantially. The SiO2 surface is already negatively charged at +0.57 V (vs. Pd/H2), and the negative surface charge is doubled when negative potentials are applied, in contrast with the CaF2 case, where the positive charge is reduced when negative potentials are applied. Interestingly, the charging of the graphene sheet is almost identical between the negatively charged SiO2 surface and positively charged CaF2 surface, demonstrating that the graphene charging is decoupled from the charging of the substrates.

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities.

The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (R2 = 0.6476 for Km and R2 = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes.

Depth-profiling alkyl chain order in unsaturated lipid monolayers on water.

Unsaturated lipids with C=C groups in their alkyl chains are widely present in the cell membrane and food. The C=C groups alter the lipid packing density, membrane stability, and persistence against lipid oxidation. Yet, molecular-level insights into the structure of the unsaturated lipids remain scarce. Here, we probe the molecular structure and organization of monolayers of unsaturated lipids on the water surface using heterodyne-detected sum-frequency generation (HD-SFG) spectroscopy. We vary the location of the C=C in the alkyl chain and find that at high lipid density, the location of the C=C group affects neither the interfacial water organization nor the tail of the alkyl chain. Based on this observation, we use the C=C stretch HD-SFG response to depth-profile the alkyl chain conformation of the unsaturated lipid. We find that the first 1/3 of carbon atoms from the headgroup are relatively rigid, oriented perpendicular to the surface. In contrast, the remaining carbon atoms can be approximated as free rotators, introducing the disordering of the alkyl chains.

Traceability of gushing water in the MiddleRoute of the South-to-North Water Diversion (Beijing section) through the river area.

To trace the origin of the gushing water in the riverine area of the Beijing section of The Middle Route of South-to-North Water Diversion Project, a dataset was established comprising water chemistry, three-dimensional fluorescence spectra, and stable isotopes for different water bodies. Results indicated significant differences in Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Ca2+ concentration among the gushing water, river water, and the water from the Middle Route of South-to-North Water Diversion Project (MRSD). Analysis using parallel factor analysis (PARAFAC) and fluorescence index revealed that dissolved organic matter (DOM) in the MRSD mainly originated from endogenous sources, while the river water and gushing water showed influences from both endogenous and exogenous sources. Nitrate sources varied among the water bodies, with distinct contributions from domestic sewage and fertilizer sources. The evaporation lines of river water and gushing water exhibited similar intercepts and slopes, but their intercepts and slopes are much smaller than those of the MRSD, suggesting stronger kinetic evaporative fractionation. In conclusion, the gushing water in the riverine area of the MRSD was determined to originate from the river, providing a fast and efficient method for gushing water source identification.

Heterodyne-Detected Sum-Frequency Generation Vibrational Spectroscopy Reveals Aqueous Molecular Structure at the Suspended Graphene/Water Interface.

Graphene, a transparent two-dimensional conductive material, has brought extensive new perspectives and prospects to various aqueous technological systems, such as desalination membranes, chemical sensors, energy storage, and energy conversion devices. Yet, the molecular-level details of graphene in contact with aqueous electrolytes, such as water orientation and hydrogen bond structure, remain elusive or controversial. Here, we employ surface-specific heterodyne-detected sum-frequency generation (HD-SFG) vibrational spectroscopy to re-examine the water molecular structure at a freely suspended graphene/water interface. We compare the response from the air/graphene/water system to that from the air/water interface. Our results indicate that the χ y y z 2 ${{\chi }_{yyz}^{\left(2\right)}}$ spectrum recorded from the air/graphene/water system arises from the topmost 1-2 water layers in contact with the graphene, with the graphene itself not generating a significant SFG response. Compared to the air/water interface response, the presence of monolayer graphene weakly affects the interfacial water. Graphene weakly affects the dangling O-H group, lowering its frequency through its interaction with the graphene sheet, and has a very small effect on the hydrogen-bonded O-H group. Molecular dynamics simulations confirm our experimental observation. Our work provides molecular insight into the interfacial structure at a suspended graphene/water interface, relevant to various technological applications of graphene.

Jujube metabolome selection determined the edible properties acquired during domestication.

Plants supply both food and medicinal compounds, which are ascribed to diverse metabolites produced by plants. However, studies on domestication-driven changes in the metabolome and genetic basis of bioactive molecules in perennial fruit trees are generally lacking. Here, we conducted multidimensional analyses revealing a singular domestication event involving the genomic and metabolomic selection of jujube trees (Ziziphus jujuba Mill.). The genomic selection for domesticated genes was highly enriched in metabolic pathways, including carbohydrates and specialized metabolism. Domesticated metabolome profiling indicated that 187 metabolites exhibited significant divergence as a result of directional selection. Malic acid was directly selected during domestication, and the simultaneous selection of specialized metabolites, including triterpenes, consequently lead to edible properties. Cyclopeptide alkaloids (CPAs) were specifically targeted for the divergence between dry and fresh cultivars. We identified 1080 significantly associated loci for 986 metabolites. Among them, 15 triterpenes were directly selected at six major loci, allowing the identification of a homologous cluster containing seven 2,3-oxidosqualene cyclases (OSCs). An OSC gene was found to contribute to the reduction in the content of triterpenes during domestication. The complete pathway for synthesizing ursolic acid was dissected by integration of the metabolome and transcriptome. Additionally, an N-methyltransferase involved in the biosynthesis of CPA and responsible for inter-cultivar content variation was identified. The present study promotes our understanding of the selection process of the global metabolome subsequent to fruit tree domestication and facilitates the genetic manipulation of specialized metabolites to enhance their edible traits.

Unique Visualization Growth Process of Long-Lived Room Temperature Phosphorescence in Polymer System.

Recently, embedding organic phosphors into the polyvinyl alcohol (PVA) matrix has emerged as a convenient strategy to obtain efficient long-lived room temperature phosphorescence (RTP) via forming strong intermolecular hydrogen bonds with organic phosphors to minimize nonradiative relaxations. Regrettably, it is discovered that PVA is unable to trigger RTP emission when a novel functional phosphor THBE containing six extended biphenyl formaldehyde arms is doped into PVA matrix. Surprisingly, the excellent long-lived RTP emission can be easily obtained by doping THBE into PVA analogs, poly(vinyl alcohol-co-ethylene) (PVA-co-PE). The unique visualization growth process (i.e., white streak generation) of long-lived RTP is observed by UV light-driven aggregation of functional molecules THBE in PVA-co-PE matrix. The phosphorescent intensity of the luminescent film is enhanced by 55 times, from 729 to 40,785 a.u., and its phosphorescence lifetime is increased by 38 times, from 37.08 to 1415.41 ms. Due to the dynamically reversible RTP performance, as well as the permeability, flexibility, and wrinkle-free properties of the luminescent film, it can be utilized to create cutting-edge information storage devices.

Enabling Long-Lived Polymeric Room Temperature Phosphorescence Material in Abominable Solvent.

Long-lived polymeric room temperature phosphorescence (RTP) materials have drawn more attention due to their convenient preparation process and equally efficient phosphorescence performance in recent years. As the polymer matrix is sensitive to air and humidity, some non-covalent interactions in the matrix are easily decomposed in water or air, which means that it is difficult for this material to be stored stably for a long time in the atmospheric environment or under harsh conditions. In this work, polymer powder mBPipQ contains aromatic and piperidine rings that are designed and synthesized successfully. Then the polymer is uniformly dispersed into epoxy resin matrix to form long-lived polymeric RTP material with efficient afterglow properties. The stiff backbone structure of mBPip and dense molecular arrangement of epoxy resin provide a rigid environment to stabilize triplet excitons, the RTP performance is greatly enhanced. The lifetime of mBPipQ in epoxy resin is 2 times higher than that of small molecule chromophore in that one. Interestingly, after soaking in strong acid or alkali solution for 10 days, the material can still emit stable and efficient long-lived phosphorescence. It is thanks to the hard matrix after full curing, which can provide a protective layer to prevent external quenchers from interfering with phosphorescence emission. Utilizing the efficient phosphorescence emission and excellent abominable-solvent resistance of this RTP material, multilevel information encryption has been successfully demonstrated. This work broadens the application scope of polymeric RTP materials in harsh environments and provides a new idea for achieving efficient RTP emission.

Structured liquids stabilized by polyethyleneimine surfactants.

Using host-guest interactions between ß-cyclodextrin-modified branched polyethyleneimine and ferrocene-terminated poly-L-lactide, the formation, assembly and jamming of polyethyleneimine surfactants (PEISs) at the liquid-liquid interface is presented. With PEIS, reconfigurable liquids with electrochemical redox responsiveness can be constructed. In conjunction with microfluidic methods, continuous, selective diffusion and purification of ionic species can be achieved in all-liquid constructs.

Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects.

Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge transference. This synergistic integration propels DTL to the forefront of research and development within the Intelligent Fault Diagnosis (IFD) sphere. While the early DTL paradigms, reliant on fine-tuning, demonstrated effectiveness, they encountered considerable obstacles in complex domains. In response to these challenges, Adversarial Deep Transfer Learning (ADTL) emerged. This review first categorizes ADTL into non-generative and generative models. The former expands upon traditional DTL, focusing on the efficient transference of features and mapping relationships, while the latter employs technologies such as Generative Adversarial Networks (GANs) to facilitate feature transformation. A thorough examination of the recent advancements of ADTL in the IFD field follows. The review concludes by summarizing the current challenges and future directions for DTL in fault diagnosis, including issues such as data imbalance, negative transfer, and adversarial training stability. Through this cohesive analysis, this review aims to offer valuable insights and guidance for the optimization and implementation of ADTL in real-world industrial scenarios.

Fire Detection in Ship Engine Rooms Based on Deep Learning.

Ship fires are one of the main factors that endanger the safety of ships; because the ship is far away from land, the fire can be difficult to extinguish and could often cause huge losses. The engine room has many pieces of equipment and is the principal place of fire; however, due to its complex internal environment, it can bring many difficulties to the task of fire detection. The traditional detection methods have their own limitations, but fire detection using deep learning technology has the characteristics of high detection speed and accuracy. In this paper, we improve the YOLOv7-tiny model to enhance its detection performance. Firstly, partial convolution (PConv) and coordinate attention (CA) mechanisms are introduced into the model to improve its detection speed and feature extraction ability. Then, SIoU is used as a loss function to accelerate the model's convergence and improve accuracy. Finally, the experimental results on the dataset of the ship engine room fire made by us shows that the mAP@0.5 of the improved model is increased by 2.6%, and the speed is increased by 10 fps, which can meet the needs of engine room fire detection.

Shading-Dependent Greening Process of the Leaves in the Light-Sensitive Albino Tea Plant 'Huangjinya': Possible Involvement of the Light-Harvesting Complex II Subunit of Photosystem II in the Phenotypic Characteristic.

The light-sensitive albino tea plant can produce pale-yellow shoots with high levels of amino acids which are suitable to process high-quality tea. In order to understand the mechanism of the albino phenotype formation, the changes in the physio-chemical characteristics, chloroplast ultrastructure, chlorophyll-binding proteins, and the relevant gene expression were comprehensively investigated in the leaves of the light-sensitive albino cultivar 'Huangjinya' ('HJY') during short-term shading treatment. In the content of photosynthetic pigments, the ultrastructure of the chloroplast, and parameters of the photosynthesis in the leaves of 'HJY' could be gradually normalized along with the extension of the shading time, resulting in the leaf color transformed from pale yellow to green. BN-PAGE and SDS-PAGE revealed that function restoration of the photosynthetic apparatus was attributed to the proper formation of the pigment-protein complexes on the thylakoid membrane that benefited from the increased levels of the LHCII subunits in the shaded leaves of 'HJY', indicating the low level of LHCII subunits, especially the lack of the Lhcb1 might be responsible for the albino phenotype of the 'HJY' under natural light condition. The deficiency of the Lhcb1 was mainly subject to the strongly suppressed expression of the Lhcb1.x which might be modulated by the chloroplast retrograde signaling pathway GUN1 (GENOMES UNCOUPLED 1)-PTM (PHD type transcription factor with transmembrane domains)-ABI4 (ABSCISIC ACID INSENSITIVE 4).

Direct Probe of Electrochemical Pseudocapacitive pH Jump at a Graphene Electrode.

Molecular-level insight into interfacial water at a buried electrode interface is essential in electrochemistry, but spectroscopic probing of the interface remains challenging. Here, using surface-specific heterodyne-detected sum-frequency generation (HD-SFG) spectroscopy, we directly access the interfacial water in contact with the graphene electrode supported on calcium fluoride (CaF2 ). We find phase transition-like variations of the HD-SFG spectra vs. applied potentials, which arises not from the charging/discharging of graphene but from the charging/discharging of the CaF2 substrate through the pseudocapacitive process. The potential-dependent spectra are nearly identical to the pH-dependent spectra, evidencing that the pseudocapacitive behavior is associated with a substantial local pH change induced by water dissociation between the CaF2 and graphene. Our work evidences the local molecular-level effects of pseudocapacitive charging at an electrode/aqueous electrolyte interface.

Nature of Cations Critically Affects Water at the Negatively Charged Silica Interface.

Understanding the collective behavior of ions at charged surfaces is of paramount importance for geological and electrochemical processes. Ions screen the surface charge, and interfacial fields break the centro-symmetry near the surface, which can be probed using second-order nonlinear spectroscopies. The effect of electrolyte concentration on the nonlinear optical response has been semi-quantitatively explained by mean-field models based on the Poisson-Boltzmann equation. Yet, to explain previously reported ion-specific effects on the spectroscopic response, drastic ion-specific changes in the interfacial properties, including surface acidities and dielectric permittivities, or strong ion adsorption/desorption had to be invoked. Here, we use sum-frequency generation (SFG) spectroscopy to probe the symmetry-breaking of water molecules at a charged silica surface in contact with alkaline metal chloride solutions (LiCl, NaCl, KCl, and CsCl) at various concentrations. We find that the water response varies with the cation: the SFG response is markedly enhanced for LiCl compared to CsCl. We show that within mean-field models, neither specific ion-surface interactions nor a reduced dielectric constant of water near the interface can account for the variation of spectral intensities with cation nature. Molecular dynamics simulations confirm that the decay of the electrochemical potential only weakly depends on the salt type. Instead, the effect of different salts on the optical response is indirect, through the reorganization of the interfacial water: the salt-type-dependent alignment of water directly at the interface can explain the observations.

Polarization-Dependent Sum-Frequency Generation Spectroscopy for Ångstrom-Scale Depth Profiling of Molecules at Interfaces.

The three-dimensional spatial distribution of molecules at soft matter interfaces is crucial for processes ranging from membrane biophysics to atmospheric chemistry. While several techniques can access surface composition, obtaining information on the depth distribution is challenging. We develop a noninvasive, polarization-resolved, surface-specific sum-frequency generation spectroscopy providing quantitative depth information. We demonstrate the technique on formic acid molecules at the air-water interface. With increasing molar fraction from 2.5% to 10%, the formic acid molecules shift, on average, ∼0.9 Å into the bulk. The consistency with the simulation data manifests that the technique allows for probing the Ångstrom-scale depth profile.

Resonance in Atomic-Scale Sliding Friction.

Friction represents a major energy dissipation mode, yet the atomistic mechanism of how friction converts mechanical motion into heat remains elusive. It has been suggested that excess phonons are mainly excited at the washboard frequency, the fundamental frequency at which relative motion excites the interface atoms, and the subsequent thermalization of these nonequilibrium phonons completes the energy dissipation process. Through combined atomic force microscopy measurements and atomistic modeling, here we show that the nonlinear interactions between a sliding tip and the substrate can generate excess phonons at not only the washboard frequency but also its harmonics. These nonequilibrium phonons can induce resonant vibration of the tip and lead to multiple peaks in the friction force as the tip sliding velocity ramps up. These observations disclose previously unrecognized energy dissipation channels associated with tip vibration and provide insights into engineering friction force through adjusting the resonant frequency of the tip-substrate system.