CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord.

Ageing is a critical factor in spinal-cord-associated disorders1, yet the ageing-specific mechanisms underlying this relationship remain poorly understood. Here, to address this knowledge gap, we combined single-nucleus RNA-sequencing analysis with behavioural and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord ageing. As an underlying mechanism, we identified a neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons, and they have the ability to trigger senescence, partly by activating SMAD signalling. We further validated the driving role of secreted CHIT1 on MN senescence using multimodal experiments both in vivo, using the NHP spinal cord as a model, and in vitro, using a sophisticated system modelling the human motor-neuron-microenvironment interplay. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.

INDUCER OF CBF EXPRESSION 1 promotes cold-enhanced immunity by directly activating salicylic acid signaling.

Cold stress affects plant immune responses, and this process may involve the salicylic acid (SA) signaling pathway. However, the underlying mechanism by which low temperature signals coordinate with SA signaling to regulate plant immunity remains unclear. Here, we found that low temperatures enhanced the disease resistance of Arabidopsis thaliana against Pseudomonas syringae pv. tomato (Pst) DC3000. This process required INDUCER OF CBF EXPRESSION 1 (ICE1), the core transcription factor in cold-signal cascades. ICE1 physically interacted with NON-EXPRESSER OF PR GENES 1 (NPR1), the master regulator of the SA signaling pathway. Enrichment of ICE1 on the PATHOGENESIS-RELATED GENE 1 (PR1) promoter and its ability to transcriptionally activate PR1 were enhanced by NPR1. Further analyses revealed that cold stress signals cooperate with SA signals to facilitate plant immunity against pathogen attack in an ICE1-dependent manner. Cold treatment promoted interactions of NPR1 and TGACG-BINDING FACTOR 3 (TGA3) with ICE1, and increased the ability of the ICE1-TGA3 complex to transcriptionally activate PR1. Together, our results characterize a critical role of ICE1 as an indispensable regulatory node linking low temperature-activated and SA-regulated immunity. Understanding this crucial role of ICE1 in coordinating multiple signals associated with immunity broadens our understanding of plant-pathogen interactions.

Threshold current density for diffusion-controlled stability of electrolytic surface nanobubbles.

Understanding the stability mechanism of surface micro/nanobubbles adhered to gas-evolving electrodes is essential for improving the efficiency of water electrolysis, which is known to be hindered by the bubble coverage on electrodes. Using molecular simulations, the diffusion-controlled evolution of single electrolytic nanobubbles on wettability-patterned nanoelectrodes is investigated. These nanoelectrodes feature hydrophobic islands as preferential nucleation sites and allow the growth of nanobubbles in the pinning mode. In these simulations, a threshold current density distinguishing stable nanobubbles from unstable nanobubbles is found. When the current density remains below the threshold value, nucleated nanobubbles grow to their equilibrium states, maintaining their nanoscopic size. However, for the current density above the threshold value, nanobubbles undergo unlimited growth and can eventually detach due to buoyancy. Increasing the pinning length of nanobubbles increases the degree of nanobubble instability. By connecting the current density with the local gas oversaturation, an extension of the stability theory for surface nanobubbles [Lohse and Zhang, Phys. Rev. E 91, 031003(R) (2015)] accurately predicts the nanobubble behavior found in molecular simulations, including equilibrium contact angles and the threshold current density. For larger systems that are not accessible to molecular simulations, continuum numerical simulations with the finite difference method combined with the immersed boundary method are performed, again demonstrating good agreement between numerics and theories.

Conformation-driven strategy for resilient and functional protein materials.

The exceptional elastic resilience of some protein materials underlies essential biomechanical functions with broad interest in biomedical fields. However, molecular design of elastic resilience is restricted to amino acid sequences of a handful of naturally occurring resilient proteins such as resilin and elastin. Here, we exploit non-resilin/elastin sequences that adopt kinetically stabilized, random coil-dominated conformations to achieve near-perfect resilience comparable with that of resilin and elastin. We also show a direct correlation between resilience and Raman-characterized protein conformations. Furthermore, we demonstrate that metastable conformation of proteins enables the construction of mechanically graded protein materials that exhibit spatially controlled conformations and resilience. These results offer insights into molecular mechanisms of protein elastomers and outline a general conformation-driven strategy for developing resilient and functional protein materials.

The influence of radiation-induced bystander effect in osteoblasts mediated by plasma-derived extracellular vesicles (EVs).

OBJECTIVES: Head and neck tumor patients may develop post-radiotherapy diseases after radiotherapy treatment. And radiotherapy can elicit radiation-induced bystander effect, wherein extracellular vesicles (EVs) play a crucial role. For normal parts of the body that have not been directly irradiated, the effect of EVs on them needs to be further explored. This study aims to investigate the functions of plasma-derived EVs in regulating normal osteoblasts during radiation-induced bystander effects. METHODS AND MATERIALS: Rat plasma-derived EVs were isolated and identified firstly, followed by an evaluation of their intracellular biological effects on normal osteoblasts in vitro. Transcriptome sequencing analysis and confirmations were performed to identify potential mechanisms. RESULTS: Irradiated plasma-derived EVs were found to enhance osteoblast proliferation, migration, and cell cycle progression, concurrently suppressing the expression of osteogenesis-related genes and proteins. Furthermore, these EVs attenuated the expression of osteogenesis and oxidative stress resistance related genes, while upregulating the PI3K-AKT pathway and intracellular reactive oxygen species in osteoblasts. CONCLUSIONS: Irradiated plasma-derived EVs could alter the biological effects in osteoblasts, which is closely associated with the levels of GPX1 and the PI3K-AKT signaling pathway. This suggests that plasma-derived EVs serve as a crucial factor contributing to radiation-induced bystander effect in osteoblasts.

Mo-Doped Mesoporous RuO2 Spheres as High-Performance Acidic Oxygen Evolution Reaction Electrocatalyst.

The development of highly active and acid-stable electrocatalysts for oxygen evolution reaction (OER) is of great significance for water electrolysis technology. Herein, a highly efficient molybdenum-doped mesoporous ruthenium dioxide sphere (Mo-RuO2 ) catalyst is fabricated by a facile impregnation and post-calcination method using mesoporous carbon spheres to template the mesostructure. The optimal Mo0.15 -RuO2 catalyst with Mo doping amount of 15 mol.% exhibits a significantly low overpotential of 147 mV at 10 mA cm-2 , a small Tafel slope of 38 mV decade-1 , and enhanced electrochemical stability in acidic electrolyte, far superior to the commercial RuO2 catalyst. The experimental results and theoretical analysis reveal that the remarkable electrocatalytic performance can be attributed to the large surface area of the mesoporous spherical structure, the structural robustness of the interconnected mesoporous framework, and the change in the electronic structure of Ru active sites induced by Mo doping. These excellent advantages make Mo-doped mesoporous RuO2 spheres a promising catalyst for highly efficient electrocatalytic OER in acidic media.

Self-Cascade Ce-MOF-818 Nanozyme for Sequential Hydrolysis and Oxidation.

Mimicking efficient biocatalytic cascades using nanozymes has gained enormous attention in catalytic chemistry, but it remains challenging to develop a nanozyme-based cascade system to sequentially perform the desired reactions. Particularly, the integration of sequential hydrolysis and oxidation reactions into nanozyme-based cascade systems has not yet been achieved, despite their significant roles in various domains. Herein, a self-cascade Ce-MOF-818 nanozyme for sequential hydrolysis and oxidation reactions is developed. Ce-MOF-818 is the first Ce(IV)-based heterometallic metal-organic framework constructed through the coordination of Ce and Cu to distinct groups. It is successfully synthesized using an improved solvothermal method, overcoming the challenge posed by the significant difference in the binding speeds of Ce and Cu to ligands. With excellent organophosphate hydrolase-like (Km = 42.3 µM, Kcat = 0.0208 min-1 ) and catechol oxidase-like (Km = 2589 µM, Kcat = 1.25 s-1 ) activities attributed to its bimetallic active centers, Ce-MOF-818 serves as a promising self-cascade platform for sequential hydrolysis and oxidation. Notably, its catalytic efficiency surpasses that of physically mixed nanozymes by approximately fourfold, owning to the close integration of active sites. The developed hydrolysis-oxidation self-cascade nanozyme has promising potential applications in catalytic chemistry and provides valuable insights into the rational design of nanozyme-based cascade systems.

A Zinc Oxide Nanowire-Modified Mineralized Collagen Scaffold Promotes Infectious Bone Regeneration.

Bone infection poses a major clinical challenge that can hinder patient recovery and exacerbate postoperative complications. This study has developed a bioactive composite scaffold through the co-assembly and intrafibrillar mineralization of collagen fibrils and zinc oxide (ZnO) nanowires (IMC/ZnO). The IMC/ZnO exhibits bone-like hierarchical structures and enhances capabilities for osteogenesis, antibacterial activity, and bacteria-infected bone healing. During co-cultivation with human bone marrow mesenchymal stem cells (BMMSCs), the IMC/ZnO improves BMMSC adhesion, proliferation, and osteogenic differentiation even under inflammatory conditions. Moreover, it suppresses the activity of Gram-negative Porphyromonas gingivalis and Gram-positive Streptococcus mutans by releasing zinc ions within the acidic infectious microenvironment. In vivo, the IMC/ZnO enables near-complete healing of infected bone defects within the intricate oral bacterial milieu, which is attributed to IMC/ZnO orchestrating M2 macrophage polarization, and fostering an osteogenic and anti-inflammatory microenvironment. Overall, these findings demonstrate the promise of the bioactive scaffold IMC/ZnO for treating bacteria-infected bone defects.

Predicting the potential deterioration of Barrett's esophagus based on gut microbiota: a Mendelian randomization analysis.

Esophageal adenocarcinoma (EAC) is one of the most malignant tumors in the digestive system. To make thing worse, the scarcity of treatment options is disheartening. However, if detected early, there is a possibility of reversing the condition. Unfortunately, there is still a lack of relevant early screening methods. Considering that Barrett's esophagus (BE), a precursor lesion of EAC, has been confirmed as the only known precursor of EAC. Analyzing which BE cases will progress to EAC and understanding the processes and mechanisms involved is of great significance for early screening of such patients. Considering the significant alterations in the gut microbiota of patients with BE and its potential role in the progression to EAC, this study aims to analyze the relationship between BE, EAC, and GM to identify potential diagnostic biomarkers and therapeutic targets. This study utilized comprehensive statistical data on gut microbiota from a large-scale genome-wide association meta-analysis conducted by the MiBioGen consortium (n = 18,340). Subsequently, we selected a set of single nucleotide polymorphisms (SNPs) that fell below the genome-wide significance threshold (1 × 10-5) as instrumental variables. To investigate the causal relationship between gut microbiota and BE and EAC, we employed various MR analysis methods, including Inverse Variance Weighting (IVW), MR-Egger regression, weighted median (WM), and weighted mean. Additionally, we assessed the level of pleiotropy, heterogeneity, and stability of genetic variations through MR-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis. Furthermore, we conducted reverse MR analysis to identify the causal relationships between gut microbiota and BE and EAC. The results from the Inverse Variance-Weighted (IVW) analysis indicate that Alistipes (P = 4.86 × 10-2), Lactobacillus (P = 2.11 × 10-2), Prevotella 7 (P = 4.28 × 10-2), and RuminococcaceaeUCG004 (P = 4.34 × 10-2) are risk factors for Barrett's esophagus (BE), while Flavonifractor (P = 8.81 × 10-3) and RuminococcaceaeUCG004 (P = 4.99 × 10-2) are risk factors for esophageal adenocarcinoma (EAC). On the other hand, certain gut microbiota genera appear to have a protective effect against both BE and EAC. These include Eubacterium (nodatum group) (P = 4.51 × 10-2), Holdemania (P = 1.22 × 10-2), and Lactococcus (P = 3.39 × 10-2) in the BE cohort, as well as Eubacterium (hallii group) (P = 4.07 × 10-2) and Actinomyces (P = 3.62 × 10-3) in the EAC cohort. According to the results of reverse MR analysis, no significant causal effects of BE and EAC on gut microbiota were observed. Furthermore, no significant heterogeneity or pleiotropy was detected in the instrumental variables. We have established a causal relationship between the gut microbiota and BE and EAC. This study holds profound significance for screening BE patients who may be at risk of deterioration, as it can provide them with timely medical interventions to reverse the condition.

Improving Sedum plumbizincicola genetic transformation with the SpGRF4-SpGIF1 gene and the self-excision CRE/LoxP system.

MAIN CONCLUSION: S. plumbizincicola genetic transformation was optimized using a self-excision molecular-assisted transformation system by integrating the SpGRF4/SpGIF1 gene with XVE and Cre/loxP. Sedum plumbizincicola, despite being an excellent hyperaccumulator of cadmium and zinc with significant potential for soil pollution phytoremediation on farmland, has nonetheless trailed behind other major model plants in genetic transformation technology. In this study, different explants and SpGRF4-SpGIF1 genes were used to optimize the genetic transformation of S. plumbizincicola. We found that petiole and stem segments had higher genetic transformation efficiency than cluster buds. Overexpression of SpGRF4-SpGIF1 could significantly improve the genetic transformation efficiency and shorten the period of obtaining regenerated buds. However, molecular assistance with overexpression of SpGRF4-SpGIF1 leads to abnormal morphology, resulting in plant tissue enlargement and abnormal growth. Therefore, we combined SpGRF4-SpGIF1 with XVE and Cre/loxP to obtain DNA autocleavage transgenic plants induced by estradiol, thereby ensuring normal growth in transgenic plants. This study optimized the S. plumbizincicola genetic transformation system, improved the efficiency of genetic transformation, and established a self-excision molecular-assisted transformation system. This work also established the basis for studying S. plumbizincicola gene function, and for S. plumbizincicola breeding and germplasm innovation.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Direct detection Φ-OTDR based on UWFBG array using linear-phase-modulated double-pulse.

We propose what we believe to be a novel direct detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on ultra-weak fiber Bragg grating (UWFBG) array to achieve distributed vibration measurements with exceptional sensitivity and remarkable stability. Our system employs a pulse modulator to generate a double pulse and achieves linear phase modulation of one pulse by one cycle through a phase modulator. The phase change can be quantitatively demodulated using our proposed N-step phase-shifted demodulation algorithm. This method effectively mitigates the influence of phase noise of the laser and the pulse modulator, while also eliminating fluctuations in the half-voltage of the phase modulator. Compared with the existing phase modulation methods, our method avoids stringent requirements for the stability and precision of phase modulation. Moreover, we propose a phase-shifted approximation method, breaking the limitation of sensing length on the traditional differential approximation method and improving the accuracy significantly. The technique's effectiveness is experimentally demonstrated on a 1 km UWFBG array with a reflectivity of -40 dB to -45 dB and a spatial resolution of 10 m. Vibrations with different amplitudes are measured quantitatively with good linearity. The low-frequency self-noise is greatly suppressed and the overall self-noise is -54.3 dB rad2/Hz.

Miniaturized high signal-to-noise ratio optical air data system using a compact multi-axis parallel transceiver for airborne application.

We have developed a miniaturized multi-channel parallel optical air data system with high signal-to-noise ratio for airborne application. In the system, we designed a fiber amplifier with multi-channel high-energy output that was respectively used as the transmitting signals and a compact multi-axis transceiver with an entrance pupil diameter of 70 mm that was used to receive multi-channel signals simultaneously. We demonstrated the performance of our system both on ground and on board. On ground, the measured line-of-sight speed had an average error of 0.02 m/s and a standard deviation of 0.15 m/s. On board, the standard deviation between the true air speed, angle of attack, and angle of sideslip measured by our system and a commercial Swiss air data system was 1 kt, 0.68°, and 0.54°, respectively, and those standard deviation between our system and a system with the same design but employing multiple single-axis telescopes with entrance pupil diameter of 30 mm was 0.34 kt, 0.36°, and 0.28°, respectively. The signal-to-noise ratio of our system was 4.5 times higher than that of the system with small single-axis telescopes. Our system is very promising for airborne applications because of its small volume, high signal-to-noise ratio, and high data rate.

Knocking down a DNA demethylase gene affects potato plant defense against a specialist insect herbivore.

DNA demethylase (DML) is involved in plant development and responses to biotic and abiotic stresses; however, its role in plant-herbivore interaction remains elusive. Here, we found that herbivory by the potato tuber moth, Phthorimaea operculella, rapidly induced the genome-wide DNA methylation and accumulation of DML gene transcripts in potato plants. Herbivory induction of DML transcripts was suppressed in jasmonate-deficient plants, whereas exogenous application of methyl jasmonate (MeJA) improved DML transcripts, indicating that the induction of DML transcripts by herbivory is associated with jasmonate signaling. Moreover, P. operculella larvae grew heavier on DML gene (StDML2) knockdown plants than on wild-type plants, and the decreased biosynthesis of jasmonates in the former may be responsible for this difference, since the larvae feeding on these two genotypes supplemented with MeJA showed similar growth. In addition, P. operculella adult moths preferred to oviposit on StDML2 knockdown plants than on wild-type plants, which was associated with the reduced emission of ß-caryophyllene in the former. In addition, supplementing ß-caryophyllene to these two genotypes further disrupted moths' oviposit choice preference for them. Interestingly, in StDML2 knockdown plants, hypermethylation was found at the promoter regions for the key genes StAOS and StAOC in the jasmonate biosynthetic pathway, as well as for the key gene StTPS12 in ß-caryophyllene production. Our findings suggest that knocking down StDML2 can affect herbivore defense via jasmonate signaling and defense compound production in potato plants.

Acupuncture plus topiramate placebo versus topiramate plus sham acupuncture for the preventive treatment of chronic migraine: A single-blind, double-dummy, randomized controlled trial.

BACKGROUND: Acupuncture has been used for the treatment of chronic migraine, but high-quality evidence is scarce. We aimed to evaluate acupuncture's efficacy and safety compared to topiramate for chronic migraine. METHODS: This double-dummy randomized controlled trial included participants aged 18-65 years diagnosed with chronic migraine. They were randomly assigned (1:1) to receive acupuncture (three sessions/week) plus topiramate placebo (acupuncture group) or topiramate (50-100 mg/day) plus sham acupuncture (topiramate group) over 12 weeks, with the primary outcome being the mean change in monthly migraine days during weeks 1-12. RESULTS: Of 123 screened patients, 60 (mean age 45.8, 81.7% female) were randomly assigned to acupuncture or topiramate groups. Acupuncture demonstrated significantly greater reductions in monthly migraine days than topiramate (weeks 1-12: -2.79 [95% CI: -4.65 to -0.94, p = 0.004]; weeks 13-24: -3.25 [95% CI: -5.57 to -0.92, p = 0.007]). No severe adverse events were reported. CONCLUSIONS: Acupuncture may be safe and effective for treating chronic migraine. The efficacy of 12 weeks of acupuncture was sustained for 24 weeks and superior to that of topiramate. Acupuncture can be used as an optional preventive therapy for chronic migraine. TRIAL REGISTRATION: ISRCTN.org Identifier 13563102.

Generation of sulfones utilizing ß-sulfinyl esters as masked aryl sulfinates under redox-neutral conditions.

A method for generation of SVI sulfones from ß-sulfinyl esters (SIV) under transition-metal-free non-oxidative mild conditions is presented. Various sulfones have been achieved with moderate to excellent yields. The advantage of using ß-sulfinyl esters as masked aryl sulfinates has also been exemplified using brominated substrates. Oxygen isotope-labeling experiments indicated that the oxygen atoms incorporated into the sulfone product come from the sulfoxide of the ß-sulfinyl ester. Successive ß-elimination/O-addition/sulfinate esterification/ß-elimination processes are proposed for the mechanism of generating SVI from SIV.

NeoSCORE II: three vs four cycles of neoadjuvant sintilimab + chemotherapy for squamous non-small-cell lung cancer.

Immune checkpoint inhibitors (ICIs) plus chemotherapy has demonstrated efficacy in resectable non-small-cell lung cancer (NSCLC), yet the optimal period of neoadjuvant immunochemotherapy is undetermined. In a phase II study (neoSCORE, NCT04459611), more neoadjuvant therapy cycles appeared to provide greater pathological remission, and patients with squamous NSCLC had a better major pathological response rate than those with nonsquamous NSCLC. Sintilimab, a monoclonal anti-PD-1 antibody, has shown encouraging antitumor activity and safety in multiple cancers, including NSCLC. Here, we describe the study design of neoSCORE II (NCT05429463), a randomized, open-label, multicenter phase III trial comparing the efficacy and safety of three cycles with four cycles of neoadjuvant sintilimab plus platinum-based chemotherapy in resectable stage IIA-IIIB squamous NSCLC. Trial registration number: NCT05429463 (ClinicalTrials.gov).

Synthesis of Fe-N doped porous carbon/silicate composites regulated by minerals in coal gasification fine slag for synergistic electrocatalytic treatment of phenolic wastewater.

Coal gasification fine slag (CGFS), as a difficult-to-dispose solid waste in the coal chemical industry, consists of minerals and residual carbon. Due to the aggregate structure of minerals blocking pores and encapsulating active substances, the high-value utilization of CGFS still remains a challenge. Based on the intrinsic characteristics of CGFS, this study synthesized Fe-N doped porous carbon/silicate composites (Fe-NC) by alkali activation and pyrolysis for electrocatalytic degradation of phenolic wastewater. Meanwhile, minerals were utilized to regulate the surface chemical and pore structure, turning their disadvantages into advantages, which caused a sharp increase in m-cresol mineralization. The positive effect of minerals on composite properties was investigated by characterization techniques, electrochemical analyses and density functional theory (DFT) calculations. It was found that the mesoporous structure of the mineral-regulated composites was further developed, with more carbon defects and reactive substances on its surface. Most importantly, silicate mediated iron conversion through strong interaction with H2O2, high work function gradient with electroactive iron, and excellent superoxide radical (•O2-) production capacity. It effectively improved the reversibility and kinetics of the entire electrocatalytic reaction. Within the Fe-NC311 electrocatalytic system, the m-cresol removal rate reached 99.55 ± 1.24%, surpassing most reported Fe-N-doped electrocatalysts. In addition, the adsorption and electrooxidation experiment confirmed that the synergistic effect of Fe-N doped porous carbon and silicate simultaneously promoted the capture of pollutants and the transformation of electroactive molecules, and hence effectively shortened the diffusion path of short-lived radicals, which was further supported by molecular dynamics simulation. Therefore, this research provides new insights into the problem of mineral limitations and opens an innovative approach for CGFS recycling and environmental remediation.