Deficiency of CBL and CBLB ubiquitin ligases leads to hyper T follicular helper cell responses and lupus by reducing BCL6 degradation.

Recent evidence reveals hyper T follicular helper (Tfh) cell responses in systemic lupus erythematosus (SLE); however, molecular mechanisms responsible for hyper Tfh cell responses and whether they cause SLE are unclear. We found that SLE patients downregulated both ubiquitin ligases, casitas B-lineage lymphoma (CBL) and CBLB (CBLs), in CD4+ T cells. T cell-specific CBLs-deficient mice developed hyper Tfh cell responses and SLE, whereas blockade of Tfh cell development in the mutant mice was sufficient to prevent SLE. ICOS was upregulated in SLE Tfh cells, whose signaling increased BCL6 by attenuating BCL6 degradation via chaperone-mediated autophagy (CMA). Conversely, CBLs restrained BCL6 expression by ubiquitinating ICOS. Blockade of BCL6 degradation was sufficient to enhance Tfh cell responses. Thus, the compromised expression of CBLs is a prevalent risk trait shared by SLE patients and causative to hyper Tfh cell responses and SLE. The ICOS-CBLs axis may be a target to treat SLE.

A stable low-temperature H2-production catalyst by crowding Pt on α-MoC.

The water-gas shift (WGS) reaction is an industrially important source of pure hydrogen (H2) at the expense of carbon monoxide and water1,2. This reaction is of interest for fuel-cell applications, but requires WGS catalysts that are durable and highly active at low temperatures3. Here we demonstrate that the structure (Pt1-Ptn)/α-MoC, where isolated platinum atoms (Pt1) and subnanometre platinum clusters (Ptn) are stabilized on α-molybdenum carbide (α-MoC), catalyses the WGS reaction even at 313 kelvin, with a hydrogen-production pathway involving direct carbon monoxide dissociation identified. We find that it is critical to crowd the α-MoC surface with Pt1 and Ptn species, which prevents oxidation of the support that would cause catalyst deactivation, as seen with gold/α-MoC (ref. 4), and gives our system high stability and a high metal-normalized turnover number of 4,300,000 moles of hydrogen per mole of platinum. We anticipate that the strategy demonstrated here will be pivotal for the design of highly active and stable catalysts for effective activation of important molecules such as water and carbon monoxide for energy production.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Correction: Farnesyl pyrophosphate is a new danger signal inducing acute cell death.

[This corrects the article DOI: 10.1371/journal.pbio.3001134.].

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

Impairments in endogenous AMPA receptor dynamics correlates with learning deficits in Alzheimer's disease model mice.

AMPA receptors (AMPARs) play a critical role in synaptic plasticity and learning and memory, and dysfunction or dysregulation of AMPARs could lead to various neurological and psychiatric disorders, such as Alzheimer's disease (AD). However, the dynamics and/or longitudinal changes of AMPARs in vivo during AD pathogenesis remain elusive. Here, employing 5xFAD SEP-GluA1 KI mice, we investigated endogenous AMPA receptor dynamics in a whisker deflection-associated Go/No-go learning paradigm. We found a significant increase in synaptosomal AMPA receptor subunits GluA1 in WT mice after learning, while no such changes were detected in 7-mo-old 5xFAD mice. Daily training led to an increase in endogenous spine surface GluA1 in Control mice, while this increase was absent in 5xFAD-KI mice which correlates with its learning defects in Go/No-go paradigm. Furthermore, we demonstrated that the onset of abnormal AMPAR dynamics corresponds temporally with microglia and astrocyte overactivation. Our results have shown that impairments in endogenous AMPA receptor dynamics play an important role in learning deficits in 5xFAD mice and AD pathogenesis.

Differential isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 mutation-related landscape in intrahepatic cholangiocarcinoma.

BACKGROUND: Patients with intrahepatic cholangiocarcinoma (ICC) are prone to recurrence and poor survival. Targeted therapy related to isocitrate dehydrogenase (IDH) is an extremely important treatment. IDH1 and IDH2 mutations are generally thought to have similar effects on the tumor landscape. However, it is doubtful whether these 2 mutations have exactly the same effects on tumor cells and the tumor microenvironment. METHODS: All collected tumor samples were subjected to simultaneous whole-exon sequencing and proteome sequencing. RESULTS: IDH1 mutations accounted for 12.2%, and IDH2 mutations accounted for 5.5%, all missense mutations. Tumors with IDH mutations had lower proportions of KRAS and TP53 mutations. Mutated genes were obviously enriched in the kinase pathway in the tumors with IDH2 mutations. The signaling pathways were mainly enriched in the activation of cellular metabolic activities and an increase of inhibitory immune cells in the tumors with IDH mutations. Moreover, tumors had unique enrichment in DNA repair in IDH1 mutants and secretion of biological molecules in IDH2 mutants. Inhibitory immune cells might be more prominent in IDH2 mutants, and the expression of immune checkpoints PVR and HLA-DQB1 was more prominent in IDH1 mutants. IDH mutants were more related to metabolism-related and inflammation-immune response clusters, and some belonged to the DNA replication and repair cluster. CONCLUSIONS: These results revealed the differential IDH1 and IDH2 mutation-related landscapes, and we have provided an important reference database to guide ICC treatment.

Preliminary investigation on the effect of Vibrio splendidus stimulation on the intestinal flora of Strongylocentrotus intermedius.

To better understand the effect of Vibrio splendidus infection on Strongylocentrotus intermedius, 16S rRNA sequencing was carried out to investigate the intestinal flora of S. intermedius stimulated by 0 CFU/mL (Con), 1.5 × 107 CFU/mL (Vib1) and 1.5 × 108 CFU/mL (Vib2) concentrations of V. splendidus. The results showed that there was significant difference in intestinal flora diversity between Con group and Vib1 group, but no significant difference between Con group and Vib2 group. However, there were significant differences in the composition of intestinal flora among all groups. Bacteroidota, Proteobacteria and Firmicutes were the dominant phylum in the Con group. The abundance of Bacteroidota and Firmicutes decreased and Proteobacteria increased in Vib1 and Vib2 groups. The relative abundance of the potential probiotic bacteria Muribaculaceae and Alloprevotella was significantly lower in the Vib1 and Vib2 groups. In addition, the opportunistic pathogen Desulfovibrio was found in Vib1 and Vib2 groups. It is evident that V. splendidus infection not only alters the composition of the microbial community in the intestinal tract of S. intermedius, but may also lead to the production of opportunistic pathogens, which could be potentially harmful to the health of S. intermedius. The results of this study provide a foundation for exploring the diseases caused by V. splendidus stimulation leading to an imbalance in the intestinal flora of S. intermedius, and contribute to our further understanding of the role of Vibrio on the health of S. intermedius.

High-Level Disordered Metal-Organic Frameworks Synthesized by Interference-Oriented Attachment for Electrochemical Anion Sieve.

Disordered MOFs seamlessly amalgamate the robust stability and pore tunability inherent in crystalline MOFs with the advantages derived from abundant defects and active sites present in amorphous structures. This study pioneers the use of the interference-oriented attachment (IOA) mechanism to meticulously craft the morphology and crystal growth of MIL-101(Cr) (Cr-MOF), resulting in the successful synthesis of a high-level disordered Cr-MOF boasting an enhanced array of active sites and exceptional electrochemical properties. The correlation between disordered structures and the electrochemical properties of MOFs are elucidated using the lattice distortion index and fractal dimension. The high-level disordered MOF electrode showcases a remarkable fluoride sieving effect, outperforming conventional fluoride removal materials with a remarkable fluoride adsorption capacity of 41.04 mgNaF gelectrodes -1. First-principles calculations, in conjunction with relevant experiments, provided further validation that the disordered structure significantly enhances the defluorination performance of the material. This study introduces a novel approach for the direct bottom-up synthesis of high-level disordered MOFs, showcasing their potential for applications in electrochemical water treatment.

Editing of ORF138 restores fertility of Ogura cytoplasmic male sterile broccoli via mitoTALENs.

Cytoplasmic male sterility (CMS), encoded by the mitochondrial open reading frames (ORFs), has long been used to economically produce crop hybrids. However, the utilization of CMS also hinders the exploitation of sterility and fertility variation in the absence of a restorer line, which in turn narrows the genetic background and reduces biodiversity. Here, we used a mitochondrial targeted transcription activator-like effector nuclease (mitoTALENs) to knock out ORF138 from the Ogura CMS broccoli hybrid. The knockout was confirmed by the amplification and re-sequencing read mapping to the mitochondrial genome. As a result, knockout of ORF138 restored the fertility of the CMS hybrid, and simultaneously manifested a cold-sensitive male sterility. ORF138 depletion is stably inherited to the next generation, allowing for direct use in the breeding process. In addition, we proposed a highly reliable and cost-effective toolkit to accelerate the life cycle of fertile lines from CMS-derived broccoli hybrids. By applying the k-mean clustering and interaction network analysis, we identified the central gene networks involved in the fertility restoration and cold-sensitive male sterility. Our study enables mitochondrial genome editing via mitoTALENs in Brassicaceae vegetable crops and provides evidence that the sex production machinery and its temperature-responsive ability are regulated by the mitochondria.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma.

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

No Effect of High Eating Frequency Compared with Low Eating Frequency on Appetite and Inflammation Biomarkers: Results from a Randomized Crossover Clinical Trial.

BACKGROUND: Eating frequency (EF) focuses on the total number of eating occasions per day and may influence metabolic health. OBJECTIVES: We sought to examine the effect of high compared with low EF on appetite regulation and inflammatory biomarkers among healthy adults. METHODS: Data are from a randomized, crossover trial (the Frequency of Eating and Satiety Hormones study). Participants (n = 50) completed 2 isocaloric 21-d study periods of low EF (3 eating occasions/d) and high EF (6 eating occasions/d) in random order with a 14-d washout period in between. Participants were free-living and consumed their own food, using study-directed, structured meal plans with identical foods and total energy in both study periods. On days 1 and 21 of each EF period, fasting blood was collected during in-person clinic visits to assess plasma concentrations of ghrelin, leptin, adiponectin, and high-sensitivity C-reactive protein (hs-CRP). Linear mixed models with EF, diet sequence, and period as fixed effects and participant as random effect were used to estimate the intervention effect. Interaction effects between EF and body fat percentage were examined. RESULTS: Among the 50 participants who completed the trial, 39 (78%) were women, 30 (60%) were Non-Hispanic White, and 40 (80%) had a body mass index of <25 kg/m2, and the mean age was 32.1 y. The differences between high and low EF in fasting ghrelin (geometric mean difference: 17.76 ng/mL; P = 0.60), leptin (geometric mean difference: 2.09 ng/mL; P = 0.14), adiponectin (geometric mean difference: 381.7 ng/mL; P = 0.32), and hs-CRP (geometric mean difference: -0.018 mg/dL; P = 0.08) were not statistically significant. No significant interaction was observed between EF and body fat percentage on appetite regulation and inflammatory biomarkers. CONCLUSIONS: No differences was observed in fasting ghrelin, leptin, adiponectin, and hs-CRP comparing high and low EF. Future studies are needed to understand the physiology of EF and appetite as they relate to metabolic health. This trial was registered at clinicaltrials.gov as NCT02392897.

Farnesyl pyrophosphate is a new danger signal inducing acute cell death.

Cell death is a vital event in life. Infections and injuries cause lytic cell death, which gives rise to danger signals that can further induce cell death, inflammation, and tissue damage. The mevalonate (MVA) pathway is an essential, highly conserved and dynamic metabolic pathway. Here, we discover that farnesyl pyrophosphate (FPP), a metabolic intermediate of the MVA pathway, functions as a newly identified danger signal to trigger acute cell death leading to neuron loss in stroke. Harboring both a hydrophobic 15-carbon isoprenyl chain and a heavily charged pyrophosphate head, FPP leads to acute cell death independent of its downstream metabolic pathways. Mechanistically, extracellular calcium influx and the cation channel transient receptor potential melastatin 2 (TRPM2) exhibit essential roles in FPP-induced cell death. FPP activates TRPM2 opening for ion influx. Furthermore, in terms of a mouse model constructing by middle cerebral artery occlusion (MCAO), FPP accumulates in the brain, which indicates the function of the FPP and TRPM2 danger signal axis in ischemic injury. Overall, our data have revealed a novel function of the MVA pathway intermediate metabolite FPP as a danger signal via transient receptor potential cation channels.

Pallidum volume as a predictor for the effectiveness of mindfulness-based cognitive therapy and psycho-education in unmedicated patients with obsessive-compulsive disorder.

BACKGROUND: Mindfulness-based cognitive therapy (MBCT) has been documented to be effective in treating obsessive-compulsive disorder (OCD). However, the neurobiological basis of MBCT remains largely elusive, which makes it clinically challenging to predict which patients are more likely to respond poorly. Hence, identifying biomarkers for predicting treatment outcomes holds both scientific and clinical values. This prognostic study aims to investigate whether pre-treatment brain morphological metrics can predict the effectiveness of MBCT, compared with psycho-education (PE) as an active placebo, among patients with OCD. METHODS: A total of 32 patients with OCD were included in this prognostic study. They received magnetic resonance imaging (MRI) brain scans before treatment. Subsequently, 16 patients received 10 weeks of MBCT, while the other 16 patients underwent a 10-week PE program. The effectiveness of the treatments was primarily assessed by the reduction rate of the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score before and after the treatment. We investigated whether several predefined OCD-associated brain morphological metrics, selected based on prior published studies by the ENIGMA Consortium, could predict the treatment effectiveness. RESULTS: Both the MBCT and PE groups exhibited substantial reductions in Y-BOCS scores over 10 weeks of treatment, with the MBCT group showing a larger reduction. Notably, the pallidum total volume was associated with treatment effectiveness, irrespective of the intervention group. Specifically, a linear regression model utilizing the pre-treatment pallidum volume to predict the treatment effectiveness suggested that a one-cubic-centimeter increase in pallidum volume corresponded to a 22.3% decrease in the Y-BOCS total score reduction rate. CONCLUSIONS: Pallidum volume may serve as a promising predictor for the effectiveness of MBCT and PE, and perhaps, other treatments with the shared mechanisms by MBCT and PE, among patients with OCD.

Comparative transcriptome analysis reveals disruption of Plutella xylostella immune system by fungal peptide cyclosporin C.

The diamondback moth (Plutella xylostella), a major threat to crucifers across the globe, has developed resistance against the majority of insecticides enhancing the need for alternate control measures against this pest. Recently cyclosporin C, a secondary metabolite produced by the insect pathogenic fungus Purpeocillium lilacinum, has been reported to induce lethal and sub-lethal effects against P. xylostella. To date, little is known about the molecular mechanisms of interaction between cyclosporin C and P. xylostella immune systems. This study reports the transcriptome-based immune response of P. xylostella to cyclosprin C treatment. Our results showed differential expression of 322, 97, and 504 differentially expressed genes (DEGS) in P. xylostella treated with cyclosporin C compared to control 24, 48, and 72 h post-treatment, respectively. Thirteen DEGs were commonly expressed at different time intervals in P. xylostella larvae treated with cyclosporin C compared to control. Cyclosporin C treatment induced the down-regulated expression of majority of immune-related genes related to pattern recognition responses, signal modulation, Toll and IMD pathways, antimicrobial peptides and antioxidant responses confirming the ability to suppress immune response of P. xylostella. These results will further improve our knowledge of the infection mechanism and complex biochemical processes involved in interaction between cyclosporin C and insect immune systems.

Amygdala-hippocampal innervation modulates stress-induced depressive-like behaviors through AMPA receptors.

Chronic stress is one of the most critical factors in the onset of depressive disorders; hence, environmental factors such as psychosocial stress are commonly used to induce depressive-​like traits in animal models of depression. Ventral CA1 (vCA1) in hippocampus and basal lateral amygdala (BLA) are critical sites during chronic stress-induced alterations in depressive subjects; however, the underlying neural mechanisms remain unclear. Here we employed chronic unpredictable mild stress (CUMS) to model depression in mice and found that the activity of the posterior BLA to vCA1 (pBLA-vCA1) innervation was markedly reduced. Mice subjected to CUMS showed reduction in dendritic complexity, spine density, and synaptosomal AMPA receptors (AMPARs). Stimulation of pBLA-vCA1 innervation via chemogenetics or administration of cannabidiol (CBD) could reverse CUMS-induced synaptosomal AMPAR decrease and efficiently alleviate depressive-like behaviors in mice. These findings demonstrate a critical role for AMPARs and CBD modulation of pBLA-vCA1 innervation in CUMS-induced depressive-like behaviors.

The Aerial Guide Dog: A Low-Cognitive-Load Indoor Electronic Travel Aid for Visually Impaired Individuals.

Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid for individuals with visual impairments. Utilizing human instinctive compliance in response to external force, we introduce the "Aerial Guide Dog", a helium balloon aerostat drone designed for indoor guidance, which leverages gentle tugs in real time for directional guidance, ensuring a seamless and intuitive guiding experience. The introduced Aerial Guide Dog has been evaluated in terms of directional guidance and path following in the pilot study, focusing on assessing its accuracy in orientation and the overall performance in navigation. Preliminary results show that the Aerial Guide Dog, utilizing Ultra-Wideband (UWB) spatial positioning and Measurement Unit (IMU) angle sensors, consistently maintained minimal deviation from the targeting direction and designated path, while imposing negligible cognitive burdens on users while completing the guidance tasks.

Slippery Au Nanosphere Monolayers with Analyte Enrichment and SERS Enhancement Functions.

Slippery surfaces can enrich analytes from solutions into tiny dots after solvent evaporation for surface-enhanced Raman scattering (SERS) detection. Here, we make the self-assembled Au nanosphere monolayers slippery, which can not only behave as SERS substrates but also enrich the analytes during solvent evaporation. A thin silica shell was used to wrap the Au nanosphere monolayer to allow the functionalization of a slippery polydimethylsiloxane brush monolayer onto it. These slippery Au nanosphere monolayers could be easily cleaned and reused many times. When Au nanospheres were introduced into the analyte solution droplet on the slippery Au nanosphere monolayer, a 3D Au nanoparticle/analyte aggregate was formed after solvent evaporation. Both the Au nanoparticle aggregate and the underneath slippery Au nanosphere monolayer could contribute to SERS enhancement. We endow the self-assembled Au nanosphere monolayer SERS substrates with an analyte enrichment function, greatly strengthening their SERS enhancement.

Interlayer Structure Manipulation of FeOCl/MXene with Soft/Hard Interface Design for Safe Water Production Using Dechlorination Battery Deionization.

Suffering from the susceptibility to decomposition, the potential electrochemical application of FeOCl has greatly been hindered. The rational design of the soft-hard material interface can effectively address the challenge of stress concentration and thus decomposition that may occur in the electrodes during charging and discharging. Herein, interlayer structure manipulation of FeOCl/MXene using soft-hard interface design method were conducted for electrochemical dechlorination. FeOCl was encapsulated in Ti3C2Tx MXene nanosheets by electrostatic self-assembly layer by layer to form a soft-hard mechanical hierarchical structure, in which Ti3C2Tx was used as flexible buffer layers to relieve the huge volume change of FeOCl during Cl- intercalation/deintercalation and constructed a conductive network for fast charge transfer. The CDI dechlorination system of FeOCl/Ti3C2Tx delivered outstanding Cl- adsorption capacity (158.47 ± 6.98 mg g-1), rate (6.07 ± 0.35 mg g-1 min-1), and stability (over 94.49 % in 30 cycles), and achieved considerable energy recovery (21.14 ± 0.25 %). The superior dechlorination performance was proved to originate from the Fe2+/Fe3+ topochemical transformation and the deformation constraint effect of Ti3C2Tx on FeOCl. Our interfacial design strategy enables a hard-to-soft integration capacity, which can serve as a universal technology for solving the traditional problem of electrode volume expansion.