In Situ Formed Gel Polymer Electrolytes Enable Stable Solid Electrolyte Interface for High-Performance Lithium Metal Batteries.

Carbonate-based electrolytes show distinct advantages in high-voltage cathodes but generate nonuniform and mechanically fragile solid-electrolyte interphase (SEI) in lithium (Li) metal batteries. Herein, we propose a LiF-rich SEI incorporating an in situ polymerized poly(hexamethylene diisocyanate)-based gel polymer electrolyte (GPE) to improve the homogeneity and mechanical stability of SEI. Fluoroethylene carbonate (FEC) as a fluorine-based additive for building LiF-rich SEI on Li metal electrodes. With this strategy, the assembled Li symmetric batteries cycled stably for 700 h, and the formation of byproducts on the Li electrode surface was significantly inhibited. The Li/LiFePO4 battery delivered significant capacity retention (91% retention after 800 cycles) at 1 C. With high-voltage LiNi0.8Co0.1Mn0.1O2 (NCM811) as cathode, the Li/GPE-FEC/NCM811 cell delivered a discharge capacity of 168.9 mAh g-1 with a capacity retention of 82% after 300 cycles at 0.5 C. From the above, the work could assist the rapid development of high-energy-density rechargeable Li metal batteries toward remarkable performance.

Step-induced double-row pattern of interfacial water on rutile TiO2(110) under electrochemical conditions.

Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous interfaces under electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) with ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces through interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides under realistic conditions.

Unraveling the energy storage mechanism in graphene-based nonaqueous electrochemical capacitors by gap-enhanced Raman spectroscopy.

Graphene has been extensively utilized as an electrode material for nonaqueous electrochemical capacitors. However, a comprehensive understanding of the charging mechanism and ion arrangement at the graphene/electrolyte interface remain elusive. Herein, a gap-enhanced Raman spectroscopic strategy is designed to characterize the dynamic interfacial process of graphene with an adjustable number of layers, which is based on synergistic enhancement of localized surface plasmons from shell-isolated nanoparticles and a metal substrate. By employing such a strategy combined with complementary characterization techniques, we study the potential-dependent configuration of adsorbed ions and capacitance curves for graphene based on the number of layers. As the number of layers increases, the properties of graphene transform from a metalloid nature to graphite-like behavior. The charging mechanism shifts from co-ion desorption in single-layer graphene to ion exchange domination in few-layer graphene. The increase in area specific capacitance from 64 to 145 µF cm-2 is attributed to the influence on ion packing, thereby impacting the electrochemical performance. Furthermore, the potential-dependent coordination structure of lithium bis(fluorosulfonyl) imide in tetraglyme ([Li(G4)][FSI]) at graphene/electrolyte interface is revealed. This work adds to the understanding of graphene interfaces with distinct properties, offering insights for optimization of electrochemical capacitors.

A weakly coordinating-intervention strategy for modulating Na+ solvation sheathes and constructing robust interphase in sodium-metal batteries.

Constructing powerful anode/cathode interphases by modulate ion solvation structure is the principle of electrolyte design. However, the methodological and theoretical design principles of electrolyte/solvation structure and their effect on electrochemical performance are still vague. Here, we propose a cationic weakly coordinating-intervention strategy for modulating the Na+ solvation sheathes and constructing robust anode/cathode interphases in sodium-metal batteries. Unlike the local highly concentrated electrolytes, 1,2-difluorobenzene can weakly coordinate with Na+ thus transforming the solvation structure into Na+-anion-incorporated structures and strengthening anode/cathode interphases formation by combining with salt decomposition. Furthermore, the correlations between the electrode interface properties and solvation structure are revealed, which can be tuned by the weakly coordination. Ultimately, the modulated electrolyte achieves 97.5% Coulombic efficiency for 600 cycles in Na‖Cu cells at 1 mA cm-2 and a beneficial lifetime (2500 h) in Na‖Na cells. Meanwhile, Na‖PB cells have achieved long-term operation at 4.8 V, along with operation at wide temperatures.

Effects of carbon source variability on enhanced Bio-hydrogen production.

This study investigated how glucose, starch, and rapeseed oil, three common food waste components with diverse molecular and physicochemical characteristics, influenced hydrogen production and microbial communities in dark fermentation under varying carbon/nitrogen (C/N) ratios. The results indicated that glucose and starch groups, significantly increased hydrogen yields to 235 mL H2/gVS (C/N = 40) and 234 mL H2/gVS (C/N = 40), respectively, while rapeseed oil, with a lower yield of 30 mL H2/gVS (C/N = 20), demonstrated a negative impact. Additionally, an accumulation of propionate was observed with increasing carbon source complexity, suggesting that simpler carbon sources favored hydrogen production and bacterial growth. Conversely, lipid-based materials required rigorous pre-treatment to mitigate their inhibitory effects on hydrogen generation. Overall, this study underscores the importance of carbon source selection, especially glucose and starch, for enhancing hydrogen production and microbial growth in dark fermentation, while highlighting the challenges posed by lipid-rich substrates that require intensive pre-treatment to optimize yields.

Reinforcing 2D Single-Crystal Bi2O2CO3 with Additional Interlayer Carbonates by CO2-Assisted Solid-to-Solid Phase Transition.

A facile gaseous CO2 mediated solid-to-solid transformation principle is adopted to insert additional CO3 2- anions into the thin single-crystal nanosheets of Bi2O2CO3, which is built of periodic arrays of intrinsic CO3 2- anions and (Bi2O2)2+ layers. The additional CO3 2- anions create abundant defects. The Bi2O2CO3 nanosheets with rich interlayer CO3 2- exhibit superior electronic properties and charge transfer kinetics than the pristine single-crystal 2D Bi2O2CO3 and display enhanced catalytic activity in photocatalytic CO2 reduction reaction and the photocatalytic oxidative degradation of organic pollutants. This work thus illustrates interlayer engineering as a flexible means to build layered 2D materials with excellent properties.

Inspection Robot Navigation Based on Improved TD3 Algorithm.

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long-short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.

Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques.

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

In Situ Electrochemical Atomic Force Microscopy: From Interfaces to Interphases.

The electrochemical interface formed between an electrode and an electrolyte significantly affects the rate and mechanism of the electrode reaction through its structure and properties, which vary across the interface. The scope of the interface has been expanded, along with the development of energy electrochemistry, where a solid-electrolyte interphase may form on the electrode and the active materials change properties near the surface region. Developing a comprehensive understanding of electrochemical interfaces and interphases necessitates three-dimensional spatial resolution characterization. Atomic force microscopy (AFM) offers advantages of imaging and long-range force measurements. Here we assess the capabilities of AFM by comparing the force curves of different regimes and various imaging modes for in situ characterizing of electrochemical interfaces and interphases. Selected examples of progress on work related to the structures and processes of electrode surfaces, electrical double layers, and lithium battery systems are subsequently illustrated. Finally, this review provides perspectives on the future development of electrochemical AFM.

Cross-Linkable Fullerene Enables Elastic and Conductive Grain Boundaries for Efficient and Wearable Tin-Based Perovskite Solar Cells.

Tin-based perovskite solar cells (TPSCs) have received increasing attention due to their low toxicity, high theoretical efficiency, and potential applications as wearable devices. However, the inherent fast and uncontrollable crystallization process of tin-based perovskites results in high defect density in the film. Meanwhile, when fabricated into flexible devices, the prepared perovskite film exhibits inevitable brittleness and high Young's modulus, seriously weakening the mechanical stability. In this work, we design and synthesize a cross-linkable fullerene, thioctic acid functionalized C60 fulleropyrrolidinium iodide (FTAI), which has multiple interactions with perovskite components and can finely regulate the crystallization quality of perovskite film. The obtained perovskite film shows an increased grain size and a more matched energy level with the electron transport material, effectively improving the carrier extraction efficiency. The FTAI-based rigid device achieves a champion efficiency of 14.91 % with enhanced stability. More importantly, the FTAI located at the perovskite grain boundaries could spontaneously cross-link during the perovskite annealing process, which effectively improves the conductivity and elasticity of grain boundaries, thereby giving the film excellent bending resistance. Finally, the FTAI-based wearable device yields a record efficiency of 12.35 % and displays robust bending durability, retaining about 90 % of the initial efficiency after 10,000 bending times.

Riboformer: a deep learning framework for predicting context-dependent translation dynamics.

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Molecular mechanism of endophytic bacteria DX120E regulating polyamine metabolism and promoting plant growth in sugarcane.

Introduction: Sugarcane endophytic nitrogen-fixing bacterium Klebsiella variícola DX120E displayed broad impact on growth, but the exact biological mechanism, especially polyamines (PAs) role, is still meager. Methods: To reveal this relationship, the content of polyamine oxidase (PAO), PAs, reactive oxygen species (ROS)-scavenging antioxidative enzymes, phytohormones, 1-aminocyclopropane-1-carboxylic synthase (ACS), chlorophyll content, and biomass were determined in sugarcane incubated with the DX120E strain. In addition, expression levels of the genes associated with polyamine metabolism were measured by transcriptomic analysis. Results: Genomic analysis of Klebsiella variícola DX120E revealed that 39 genes were involved in polyamine metabolism, transport, and the strain secrete PAs in vitro. Following a 7-day inoculation period, DX120E stimulated an increase in the polyamine oxidase (PAO) enzyme in sugarcane leaves, however, the overall PAs content was reduced. At 15 days, the levels of PAs, ROS-scavenging antioxidative enzymes, and phytohormones showed an upward trend, especially spermidine (Spd), putrescine (Put), catalase (CAT), auxin (IAA), gibberellin (GA), and ACS showed a significant up-regulation. The GO and KEGG enrichment analysis found a total of 73 differentially expressed genes, involving in the cell wall (9), stimulus response (13), peroxidase activity (33), hormone (14) and polyamine metabolism (4). Discussion: This study demonstrated that endophytic nitrogen-fixing bacteria stimulated polyamine metabolism and phytohormones production in sugarcane plant tissues, resulting in enhanced growth. Dual RNA-seq analyses provided insight into the early-stage interaction between sugarcane seedlings and endophytic bacteria at the transcriptional level. It showed how diverse metabolic processes selectively use distinct molecules to complete the cell functions under present circumstances.

General and Scalable Synthesis of Mesoporous 2D MZrO2 (M = Co, Mn, Ni, Cu, Fe) Nanocatalysts by Amorphous-to-Crystalline Transformation.

In modern heterogeneous catalysis, it remains highly challenging to create stable, low-cost, mesoporous 2D photo-/electro-catalysts that carry atomically dispersed active sites. In this work, a general shape-preserving amorphous-to-crystalline transformation (ACT) strategy is developed to dope various transition metal (TM) heteroatoms in ZrO2, which enabled the scalable synthesis of TMs/oxide with a mesoporous 2D structure and rich defects. During the ACT process, the amorphous MZrO2 nanoparticles (M = Fe, Ni, Cu, Co, Mn) are deposited within a confined space created by the NaCl template, and they transform to crystalline 2D ACT-MZrO2 nanosheets in a shape-preserving manner. The interconnected crystalline ACT-MZrO2 nanoparticles thus inherit the same structure as the original MZrO2 precursor. Owing to its rich active sites on the surface and abundant oxygen vacancies (OVs), ACT-CoZrO2 gives superior performance in catalyzing the CO2-to-syngas conversion as demonstrated by experiments and theoretical calculations. The ACT chemistry opens a general route for the scalable synthesis of advanced catalysts with precise microstructure by reconciliating the control of crystalline morphologies and the dispersion of heteroatoms.

Self-assembled hydrated copper coordination compounds as ionic conductors for room temperature solid-state batteries.

As the core component of solid-state batteries, neither current inorganic solid-state electrolytes nor solid polymer electrolytes can simultaneously possess satisfactory ionic conductivity, electrode compatibility and processability. By incorporating efficient Li+ diffusion channels found in inorganic solid-state electrolytes and polar functional groups present in solid polymer electrolytes, it is conceivable to design inorganic-organic hybrid solid-state electrolytes to achieve true fusion and synergy in performance. Herein, we demonstrate that traditional metal coordination compounds can serve as exceptional Li+ ion conductors at room temperature through rational structural design. Specifically, we synthesize copper maleate hydrate nanoflakes via bottom-up self-assembly featuring highly-ordered 1D channels that are interconnected by Cu2+/Cu+ nodes and maleic acid ligands, alongside rich COO- groups and structural water within the channels. Benefiting from the combination of ion-hopping and coupling-dissociation mechanisms, Li+ ions can preferably transport through these channels rapidly. Thus, the Li+-implanted copper maleate hydrate solid-state electrolytes shows remarkable ionic conductivity (1.17 × 10-4 S cm-1 at room temperature), high Li+ transference number (0.77), and a 4.7 V-wide operating window. More impressively, Li+-implanted copper maleate hydrate solid-state electrolytes are demonstrated to have exceptional compatibility with both cathode and Li anode, enabling long-term stability of more than 800 cycles. This work brings new insight on exploring superior room-temperature ionic conductors based on metal coordination compounds.

Cell size homeostasis is tightly controlled throughout the cell cycle.

To achieve a stable size distribution over multiple generations, proliferating cells require a means of counteracting stochastic noise in the rate of growth, the time spent in various phases of the cell cycle, and the imprecision in the placement of the plane of cell division. In the most widely accepted model, cell size is thought to be regulated at the G1/S transition, such that cells smaller than a critical size pause at the end of G1 phase until they have accumulated mass to a predetermined size threshold, at which point the cells proceed through the rest of the cell cycle. However, a model, based solely on a specific size checkpoint at G1/S, cannot readily explain why cells with deficient G1/S control mechanisms are still able to maintain a very stable cell size distribution. Furthermore, such a model would not easily account for stochastic variation in cell size during the subsequent phases of the cell cycle, which cannot be anticipated at G1/S. To address such questions, we applied computationally enhanced quantitative phase microscopy (ceQPM) to populations of cultured human cell lines, which enables highly accurate measurement of cell dry mass of individual cells throughout the cell cycle. From these measurements, we have evaluated the factors that contribute to maintaining cell mass homeostasis at any point in the cell cycle. Our findings reveal that cell mass homeostasis is accurately maintained, despite disruptions to the normal G1/S machinery or perturbations in the rate of cell growth. Control of cell mass is generally not confined to regulation of the G1 length. Instead mass homeostasis is imposed throughout the cell cycle. In the cell lines examined, we find that the coefficient of variation (CV) in dry mass of cells in the population begins to decline well before the G1/S transition and continues to decline throughout S and G2 phases. Among the different cell types tested, the detailed response of cell growth rate to cell mass differs. However, in general, when it falls below that for exponential growth, the natural increase in the CV of cell mass is effectively constrained. We find that both mass-dependent cell cycle regulation and mass-dependent growth rate modulation contribute to reducing cell mass variation within the population. Through the interplay and coordination of these 2 processes, accurate cell mass homeostasis emerges. Such findings reveal previously unappreciated and very general principles of cell size control in proliferating cells. These same regulatory processes might also be operative in terminally differentiated cells. Further quantitative dynamical studies should lead to a better understanding of the underlying molecular mechanisms of cell size control.

Spatial Heterogeneity and Strong Coupling of FeII /FeIII in an Individual Metal-Organic Framework Nanoparticle for Efficient CO2 Photoreduction.

The synthesis and characterization of an FeII /FeIII metal-organic framework (MOF) nanocrystal with spatial heterogeneity that arises from the non-uniform distribution of different valence states is disclosed. The FeII /FeIII -Ni Prussian blue analog (PBA) delivers superior photocatalytic performance in the selective CO2 reduction reaction thanks to the strong FeII /FeIII coupling, with CO yield up to 12.27 mmol g-1 h-1 and 90.6% selectivity under visible-light irradiation. Density functional theory calculation and experimental studies prove that the spatial heterogeneity of FeII /FeIII in the individual MOF nanocrystal not only directs and expedites the charge transfer within a catalyst particle but also creates the heterogeneity of catalytically-active Ni sites for efficient CO2 photoreduction. The current findings add to a growing literature of materials with compositional heterogeneity and provide a reference for future research.

Pulsed ultrasound promotes secretion of anti-inflammatory extracellular vesicles from skeletal myotubes via elevation of intracellular calcium level.

The regulation of inflammatory responses is an important intervention in biological function and macrophages play an essential role during inflammation. Skeletal muscle is the largest organ in the human body and releases various factors which mediate anti-inflammatory/immune modulatory effects. Recently, the roles of extracellular vesicles (EVs) from a large variety of cells are reported. In particular, EVs released from skeletal muscle are attracting attention due to their therapeutic effects on dysfunctional organs and tissues. Also, ultrasound (US) promotes release of EVs from skeletal muscle. In this study, we investigated the output parameters and mechanisms of US-induced EV release enhancement and the potential of US-treated skeletal muscle-derived EVs in the regulation of inflammatory responses in macrophages. High-intensity US (3.0 W/cm2) irradiation increased EV secretion from C2C12 murine muscle cells via elevating intracellular Ca2+ level without negative effects. Moreover, US-induced EVs suppressed expression levels of pro-inflammatory factors in macrophages. miRNA sequencing analysis revealed that miR-206-3p and miR-378a-3p were especially abundant in skeletal myotube-derived EVs. In this study we demonstrated that high-intensity US promotes the release of anti-inflammatory EVs from skeletal myotubes and exert anti-inflammatory effects on macrophages.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

Regulating the electrical double layer to prevent water electrolysis for wet ionic liquids with cheap salts.

Hydrophobic ionic liquids (ILs), broadly utilized as electrolytes, face limitations in practical applications due to their hygroscopicity, which narrows their electrochemical windows via water electrolysis. Herein, we scrutinized the impact of incorporating cheap salts on the electrochemical stability of wet hydrophobic ILs. We observed that alkali ions effectively manipulate the solvation structure of water and regulate the electrical double layer (EDL) structure by subtly adjusting the free energy distribution of water in wet ILs. Specifically, alkali ions significantly disrupted the hydrogen bond network, reducing free water, strengthening the O-H bond, and lowering water activity in bulk electrolytes. This effect was particularly pronounced in EDL regions, where most water molecules were repelled from both the cathode and anode with the disappearance of the H-bond network connectivity along the EDL. The residual interfacial water underwent reorientation, inhibiting water electrolysis and thus enhancing the electrochemical window of wet hydrophobic ILs. This theoretical proposition was confirmed by cyclic voltammetry measurements, demonstrating a 45% enhancement in the electrochemical windows for salt-in-wet ILs, approximating the dry one. This work offers feasible strategies for tuning the EDL and managing interfacial water activity, expanding the comprehension of interface engineering for advanced electrochemical systems.

Supervised Multi-Layer Conditional Variational Auto-Encoder for Process Modeling and Soft Sensor.

Variational auto-encoders (VAE) have been widely used in process modeling due to the ability of deep feature extraction and noise robustness. However, the construction of a supervised VAE model still faces huge challenges. The data generated by the existing supervised VAE models are unstable and uncontrollable due to random resampling in the latent subspace, meaning the performance of prediction is greatly weakened. In this paper, a new multi-layer conditional variational auto-encoder (M-CVAE) is constructed by injecting label information into the latent subspace to control the output data generated towards the direction of the actual value. Furthermore, the label information is also used as the input with process variables in order to strengthen the correlation between input and output. Finally, a neural network layer is embedded in the encoder of the model to achieve online quality prediction. The superiority and effectiveness of the proposed method are demonstrated by two real industrial process cases that are compared with other methods.