Influence mechanism of grinding surface quality of 20CrMnTi steel on contact failure.

To reveal the influence mechanism of the grinding surface quality of 20CrMnTi steel components on the tribological characteristics and contact fatigue performance, accelerated tests for sliding friction wear and fatigue damage were carried out. Tribological characteristics and contact fatigue performance get worse with increasing surface roughness while getting better with increasing surface microhardness. Residual compressive stress is conducive to inhibiting the initiation and propagation of cracks and promoting contact fatigue performance. Additionally, mechanical friction, abrasive wear, adhesive wear and fatigue damage coexist and form a competing failure mechanism under the synergistic effect of frictional wear and contact fatigue failure. The damage process mainly manifests as wear, stress concentration induced fatigue, microcracks, pitting, and spalling in the shallow layer. This study is more beneficial to promote the 20CrMnTi steel transmission parts manufacturing products for high precision, low damage, and long life.

The association between internet use and the choice of medical institution among Chinese older adults.

BACKGROUND: As older people have complex medical needs and still encounter challenges in accessing online health information, the relationship between Internet use and the choice of medical institution made by them is unclear, and we aimed to examine this relationship. METHODS: Data from the newly released 2020 China Family Panel Survey database were used. Furthermore, we used descriptive statistics to analyze the background characteristics of the sample and a logistic regression model to estimate the impact of Internet use on the choice of medical institution made by older adults. We conducted a stratified analysis to explore the influence of different characteristics on the relationship between Internet use and the choice of medical institution. RESULTS: Totally 4,948 older adults were included. Multivariate logistic regression showed that, compared to non-Internet users, Internet users were less likely to choose community health service centers over general hospitals (P < 0.001, OR = 0.667, 95CI%: 0.558-0.797). The subgroup analyses found that Internet use only had an impact on the choice of medical institution in older adults aged 65-69 years, those with partners, those with primary or secondary education, those residing in urban areas, those without medical insurance, those with a self-rated health status as average or healthy, those with unchanged or better health trend, and those without chronic disease. The effect of Internet use on the choice of medical institution did not differ by sex, satisfaction, or trust in doctors. CONCLUSION: Internet use may significantly affect older adults' tendency to choose general hospitals to meet their daily medical needs. The subgroup analyses indicated that different characteristics of older people affected this association.

Nomogram Predicts Prognostic Factors for Head and Neck Cutaneous Melanoma: A Population-Based Analysis.

BACKGROUND: The head and neck cutaneous melanoma (HNCM) accounts for 20% of newly diagnosed melanoma. Research on prognostic models for their survival yet remains largely unexplored. This study employed a nomogram approach to develop and validate a predictive model for both overall survival (OS) and disease-specific survival (DSS) in patients with HNCM. METHODS: This study analyzed the HNCM patients diagnosed between 2004 and 2014 from Surveillance, Epidemiology, and End Results database. To identify independent prognostic factors for HNCM, we integrated results from univariate Cox regression analysis, random survival forests, and LASSO regression with cross-validation. A nomogram was designed and validated based on the identified characteristics to predict the 3-, 5-, and 8-year OS and DSS of patients with HNCM. RESULTS: Age, Stage, Ulceration, Thickness, Chemotherapy, lymph node metastasis, and Radiation were identified as independent prognostic factors. The nomogram achieved a satisfactory performance with C-indices of 0.824(DSS) and 0.757(OS) in the training cohort and 0.827(DSS) and 0.749(OS) in the validation cohort, respectively. The area under the curves for the OS at 3, 5, and 8 years were 0.789, 0.788, and 0.794 for the training cohort, and 0.778, 0.776, and 0.795 for the validation cohort, respectively. For DSS, the area under the curves at 3, 5, and 8 years were 0.859, 0.842, and 0.828 in the training cohort, and 0.864, 0.844, and 0.834 in the validation cohort, respectively. The calibration curve showed that there was a strong correlation between the observed outcomes and the predicted survival probability. CONCLUSIONS: This study established and validated predictive nomograms for HNCM patients with robust predictive performance.

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

A promising subgroup identification method based on a genetic algorithm for censored survival data.

Modern precision medicine requires drug development to account for patients' heterogeneity, as only a subgroup of the patient population is likely to benefit from the targeted therapy. In this paper, we propose a novel method for subgroup identification based on a genetic algorithm. The proposed method can detect promising subgroups defined by predictive biomarkers in which the treatment effects are much higher than the population average. The main idea is to search for the subgroup with the greatest predictive ability in the entire subgroup space via a genetic algorithm. We design a real-valued representation of subgroups that evolves according to a genetic algorithm and derive an objective function that properly evaluates the predictive ability of the subgroups. Compared with model- or tree-based subgroup identification methods, the distinctive search strategy of this new approach offers an improved capability to explore subgroups defined by multiple predictive biomarkers. By embedding a resampling scheme, the multiplicity and complexity issues inherent in subgroup identification methods can be addressed flexibly. We evaluate the performance of the proposed method in comparison with two other methods using simulation studies and a real-world example. The results show that the proposed method exhibits good properties in terms of multiplicity and complexity control, and the subgroups identified are much more accurate. Although we focus on the implementation of censored survival data, this method could easily be extended for the realization of continuous and categorical endpoints.

Identifying survival of pan-cancer patients under immunotherapy using genomic mutation signature with large sample cohorts.

Although immune checkpoint inhibitors have led to durable clinical response in multiple cancers, only a small proportion of patients respond to this treatment. Therefore, we aim to develop a predictive model that utilizes gene mutation profiles to accurately identify the survival of pan-cancer patients with immunotherapy. Here, we develop and evaluate three different nomograms using two cohorts containing 1,594 cancer patients whose mutation profiles are obtained by MSK-IMPACT sequencing and 230 cancer patients receiving whole-exome sequencing, respectively. Using eighteen genes (SETD2, BRAF, NCOA3, LATS1, IL7R, CREBBP, TET1, EPHA7, KDM5C, MET, KMT2D, RET, PAK7, CSF1R, JAK2, FAT1, ASXL1 and SPEN), the first nomogram stratifies patients from both cohorts into High-Risk and Low-Risk groups. Pan-cancer patients in the High-Risk group exhibit significantly shorter overall survival and progression-free survival than patients in the Low-Risk group in both cohorts. Meanwhile, the first nomogram also accurately identifies the survival of patients with melanoma or lung cancer undergoing immunotherapy, or pan-cancer patients treated with anti-PD-1/PD-L1 inhibitor or anti-CTLA-4 inhibitor. The model proposed is not a prognostic model for the survival of pan-cancer patients without immunotherapy, but a simple, effective and robust predictive model for pan-cancer patients' survival under immunotherapy, and could provide valuable assistance for clinical practice.

A Novel Acetylation-Immune Subtyping for the Identification of a BET Inhibitor-Sensitive Subgroup in Melanoma.

BACKGROUND: There have been significant advancements in melanoma therapies. BET inhibitors (BETis) show promise in impairing melanoma growth. However, identifying BETi-sensitive melanoma subtypes is challenging. METHODS AND RESULTS: We analyzed 48 melanoma cell lines and 104 patients and identified two acetylation-immune subtypes (ALISs) in the cell lines and three ALISs in the patients. ALIS I, with high HAT1 and low KAT2A expression, showed a higher sensitivity to the BETi JQ-1 than ALIS II. ALIS III had low HAT1 expression. The TAD2B expression was low in ALIS I and II. KAT2A and HAT1 expressions were negatively correlated with the methylation levels of their CG sites (p = 0.0004 and 0.0003). Immunological gene sets, including B cell metagenes, activated stroma-related genes, fibroblast TGF response signatures (TBRS), and T cell TBRS-related genes, were up-regulated in ALIS I. Furthermore, KAT2A played a key role in regulating BETi sensitivity. CONCLUSIONS: The sensitivity of ALIS I to the BETi JQ-1 may be due to the inhibition of BETi resistance pathways and genes by low KAT2A expression and the dysregulation of the immune microenvironment by high HAT1 expression resulting from the absence of immune cells. ALIS I had the worst progression but showed sensitivity to BETi and B-cell-related immunotherapy, despite not responding to BRAF inhibitors.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

Interactions between arsenic migration and CH4 emission in a soil bioelectrochemical system under the effect of zero-valent iron.

Dissimilatory soil arsenic (As) reduction and release are driven by microbial extracellular electron transfer (EET), while reverse EET mediates soil methane (CH4) emission. Nevertheless, the detailed biogeochemical mechanisms underlying the tight links between soil As migration and methanogenesis are unclear. This study used a bioelectrochemical-based system (BES) to explore the potential effects of zero-valent iron (ZVI) addition on "As migration-CH4 emission" interactions from chemical and microbiological perspectives. Voltage and ZVI amendment experiments showed that dissolved As was efficiently immobilized with increased CH4 production in the soil BES, As release and CH4 production exhibited a high negative exponential correlation, and reductive As dissolution could be entirely inhibited in the methanogenic stage. Gene quantification and bacterial community analysis showed that in contrast to applied voltage, ZVI changed the spatial heterogeneity of the distribution of electroactive microorganisms in the BES, significantly decreasing the relative abundance of arrA and dissimilatory As/Fe-reducing bacteria (e.g., Geobacter) while increasing the abundance of aceticlastic methanogens (Methanosaeta), which then dominated CH4 production and As immobilization after ZVI incorporation. In addition to biogeochemical activities, coprecipitation with ferric (iron) contributed 77-93% dissolved As removal under ZVI addition. This study will enhance our knowledge of the processes and microorganisms controlling soil As migration and CH4 emission.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

Synthesis of FeCoNi/C decorated graphene composites derived from trimetallic metal-organic framework as ultrathin and high-performance electromagnetic wave absorbers.

High-temperature pyrolysis of metal-organic framework (MOF) is an effective way to prepare lightweight and high-performance electromagnetic wave absorbers. In this work, iron cobalt nickel/carbon (FeCoNi/C) decorated graphene composites were fabricated by the two-step method of solvothermal reaction and pyrolysis treatment. Results of micromorphology analysis demonstrated that numerous octahedral FeCoNi/C carbon frameworks were almost uniformly distributed on the wrinkled surfaces of flaky graphene. Moreover, the electromagnetic parameters and wave absorbing properties of obtained composites were regulated through simply changing the addition amounts of graphene oxide. Significantly, the as-prepared FeCoNi/C decorated graphene composite with the addition amount of graphene oxide of 67.2 mg exhibited the best electromagnetic absorption performance. The minimum reflection loss was as large as -66 dB at 15.6 GHz and broad absorption bandwidth reached up to 4.8 GHz with an ultrathin thickness of 1.53 mm and a filler loading ratio of 30 wt%. Furthermore, the maximum absorption bandwidth was enlarged to 5.2 GHz via slightly adjusting the matching thickness to 1.56 mm. Additionally, the probable electromagnetic attenuation mechanisms of attained composites were proposed. The results of this study would provide a reference for the preparation of MOF derived carbon-based magnetic composites as ultrathin and high-performance electromagnetic absorbers.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer.

Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.

Ovarian cancer is a highly malignant gynecological disease. The high heterogeneity of ovarian cancer may contribute to chemotherapy resistance and immunotherapy insensitivity. Gene alterations and aberrant methylation occur in the process of tumor initiation and progression, but not all alterations are drivers of tumor development. In this study, we aim to find the DNA driver methylation (DNAme) that plays a decisive role in ovarian cancer development and obtain a novel multiomics molecular subtype related to DNAme integrated by multiple omics information. We identified 43 overlapping DNAme in three cohorts. The multiomics subtype associated with DNAme could predict ovarian cancer prognosis and treatment response.

Proteome and microbiota analyses characterizing dynamic coral-algae-microbe tripartite interactions under simulated rapid ocean acidification.

Ocean acidification (OA) is a pressing issue currently and in the future for coral reefs. The importance of maintenance interactions among partners of the holobiont association in the stress response is well appreciated; however, the candidate molecular and microbial mechanisms that underlie holobiont stress resilience or susceptibility remain unclear. Here, to assess the effects of rapid pH change on coral holobionts at both the protein and microbe levels, combined proteomics and microbiota analyses of the scleractinian coral Galaxea fascicularis exposed to three relevant OA scenarios, including current (pHT = 8.15), preindustrial (pHT = 8.45) and future IPCC-2100 scenarios (pHT = 7.85), were conducted. The results demonstrated that pH changes had no significant effect on the physiological calcification rate of G. fascicularis in a 10-day experiment; however, significant differences were recorded in the proteome and 16S profiling. Proteome variance analysis identified some of the core biological pathways in coral holobionts, including coral host infection and immune defence, and maintaining metabolic compatibility involved in energy homeostasis, nutrient cycling, antibiotic activity and carbon budgets of coral-Symbiodiniaceae interactions were key mechanisms in the early OA stress response. Furthermore, microbiota changes indicate substantial microbial community and functional disturbances in response to OA stress, potentially compromising holobiont health and fitness. Our results may help to elucidate many complex mechanisms to describe scleractinian coral holobiont responses to OA and raise interesting questions for future studies.

Effects of anode/cathode electroactive microorganisms on arsenic removal with organic/inorganic carbon supplied.

This study reports the effects of an external voltage (0 V, 0.4 V and 0.9 V) on soil arsenic (As) release and sequestration when amended with organic carbon (NaAc) and inorganic carbon (NaHCO3), respectively, in a soil bioelectrochemistry system (BES). The results demonstrated that although an external voltage had no effect on the As removal capacity in an oligotrophic environment fueled with NaHCO3, 93.6% of As(III) in the supernatant was removed at 0.9 V with an NaAc amendment. Interestingly, the content of As detected on the electrodes was higher than that removed from the supernatant, implying a continuous release of soil As under external voltages and rapid adsorption onto the electrodes, especially the cathode. In addition, the species of As on the cathode were similar to those in the supernatant (the As(III)/As(V) ratio was approximately 3:1), indicating that the removal capacity was independent of preoxidation. From the viewpoint of electroactive microorganisms (EABs), the relative abundances of the arrA gene and Geobacter genus were specifically enriched at the anode, thus signifying stimulation of the reduction and release of soil As in the anode region. By comparison, Bacillus was particularly abundant at the cathode, which could contribute to the oxidation and sequestration of As in the cathode region. Additionally, specific extracellular polymeric substances (EPSs) secreted by EABs could combine with As, which was followed by electrostatic attraction to the cathode under the effect of an electric field. Furthermore, the formation of secondary minerals and coprecipitation in the presence of iron (Fe) may have also contributed to As removal from solution. The insights from this study will enable us to further understand the biogeochemical cycle of soil As and to explore the feasibility of in situ As bioremediation techniques, combining the aspects of microbial and physicochemical processes in soil bioelectrochemical systems.

Incidence of and risk factors associated with lung metastases in newly diagnosed epithelial ovarian cancer with a look on prognosis after diagnosis: a population-based cohort study of the SEER database.

PURPOSE: Patients with lung metastases (LM) from epithelial ovarian cancer (EOC) (EOCLM) usually have a poor prognosis. However, there is no consensus on the optimal management of these patients. In this study, we aimed to take a look at the incidence of LM and factors associated with its occurrence as well as the prognosis in newly diagnosed EOC with LM on a population level. METHODS: EOC patients diagnosed between the years 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) program database. Multivariable logistic regression and multivariable Cox regression were used to investigate the factors that could predict the occurrence of and prognosis after diagnosis of EOC with LM. RESULTS: Of the 33,418 qualified EOC patients, 2240 (6.7%) were noted to have LMs at the time of EOC diagnosis. Higher T stage, N1 stage, advanced tumor grade, and elevated cancer antigen-125 levels were found to be associated with a higher risk of having LM at the time of EOC diagnosis. The median survival time after diagnosis with EOCLM was found to be 13.0 months (interquartile range: 3.0-34.0 months). Being unmarried and having mucinous histology were both associated with increased all-cause death risk from EOCLM. However, the primary tumor originated from the midline of ovaries, surgical management, and whether patient received chemotherapy or not predicted improved overall survival. The median survival time of patients was significantly longer for EOCLM cases managed surgically (31.0 months) versus those who did not have surgery (4.0 months), as well as EOCLM cases received chemotherapy (23.0 months) versus those who did not have chemotherapy (2.0 months). CONCLUSION: This retrospective cohort study showed that de novo LM was infrequent in EOC patients overall and when present predicted poor prognosis. The findings can be potentially useful in formulating for follow-up strategies, screening tools, and personalized interventions.

Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment.

The tumor immune microenvironment is heterogeneous, and its impact on treatment responses is not well understood. It is still a challenge to analyze the interaction between malignant cells and the tumor microenvironment to apply suitable immunotherapy in lung adenocarcinoma. We performed the nonnegative matrix factorization method to 513 messenger RNA expression profiles of lung adenocarcinomas (LUADs) from The Cancer Genome Atlas (TCGA) to obtain an immune-related expression pattern. Subsequently, we characterized the immune-related gene signatures and clinical and survival characteristics. We used 576 patients from Gene Expression Omnibus to confirm our findings. Of the patients in the training cohort, 51% had a high immune enrichment score, high expression of immune cell signaling, cytolytic activity, and interferon (IFN)-related signatures (all P < .05). We denoted these as the Immune Class. We further subdivided the Immune Class into two subclasses based on the tumor microenvironment. These were denoted the Active Immune Class and Exhausted Immune Class. The former showed significant IFN, T-cells, M1 macrophage signatures, and better prognosis (all P < .05), while the latter presented an exhausted immune response with activated stromal enrichment, M2 macrophage signatures, and immunosuppressive factors such as WNT/transforming growth factor-ß (all P < .05). Furthermore, we predicted the response of our immunophenotypes to immunological checkpoint inhibitors (P < .05). Our findings provide a novel insight into the immune-related state of LUAD and can identify the patients who will be receptive to suitable immunotherapeutic treatments.

TiO2 nanoparticles accelerate methanogenesis in mangrove wetlands sediment.

In this study, the response of methane (CH4) production to the addition of titanium dioxide nanoparticles (TiO2 NPs) with three types of short-chain fatty acids (sodium acetate, sodium propionate and sodium butyrate) as carbon sources in mangrove sediment was investigated. The results showed that the maximum CH4 formation rate increased by 45.2%, 32.7% and 48.6% and the maximum cumulative CH4 production increased by 25.2%, 7.7% and 6.3% with the addition of TiO2 NPs in the sodium acetate, sodium propionate and sodium butyrate systems, respectively. The microbial community analysis revealed that the electrogenic bacteria Proteiniclasticum and Pseudomonas, butyrate oxidizing bacteria Syntrophomonas and methanogens Methanobacterium and Methanosarcina were significantly enriched in the presence of TiO2 NPs, indicating that TiO2 NPs can enhance CH4 production by stimulating the growth of different species of methanogens and butyrate oxidizing bacteria. The enlarged distance between microbes, the enhanced conductivity of the sediment and the typical microorganisms for direct interspecies electron transfer (DIET) with the addition of TiO2 NPs suggest that the promoted DIET between distinct microorganisms could be another possible explanation for the improvement in CH4 production. It can be speculated that a weaker effect on methanogenesis increases under the natural concentration of TiO2 NPs compared with the experimental conditions; however, the amounts of TiO2 NPs are increasing enriched in wetland environments. Therefore, the findings of this study increase current knowledge about the effect of nanomaterials on global CH4 emissions and suggest that the discharge of wastewater containing TiO2 NPs from the synthesis and incorporation of TiO2 NPs in customer products needs to be monitored.

Electromagnetic and microwave absorption properties of iron pentacarbonyl pyrolysis-synthesized carbonyl iron fibers.

The present study executed iron pentacarbonyl pyrolysis to synthesize one-dimensional structured carbonyl iron fibers (CIFs) via carrier gas induced flow. The obtained CIFs with a diameter of 100-300 nm and length-diameter ratio of more than 20, are actually composed of a large number of nanocrystalline aggregates. We investigated the dependence of the structure, morphology, and static magnetic and electromagnetic properties of the CIFs on the pyrolysis temperatures. CIFs synthesized at 300 °C (denoted as CIF-300) exhibited optimal microwave absorption properties dependent on the fiber structure and well-matched impedance. An optimal reflection loss of -58.1 dB was observed at 13.8 GHz with a matching thickness of 1.43 mm. Furthermore, CIF-300 presented a broad effective absorption bandwidth (RL ≤ -10 dB) of 5.66 GHz with a thickness of 1.44 mm, indicating that it could be applied in practical applications from 3.74 GHz to 18.0 GHz by tuning its thickness from 1.0 mm to 4.0 mm. This paper not only reveals that the CIFs synthesized at 300 °C have great potential application in microwave absorbing materials (MAMs) with thin thicknesses, wide absorption bandwidths, and strong absorption intensities, but also provides a simple approach to prepare metal fibers.