Electrochemical Properties and Applications of Nanocrystalline, Microcrystalline, and Epitaxial Cubic Silicon Carbide Films

Microstructures of the materials (e.g., crystallinitiy, defects, and composition, etc.) determine their properties, which eventually lead to their diverse applications. In this contribution, the properties, especially the electrochemical properties, of cubic silicon carbide (3C-SiC) films have been engineered by controlling their microstructures. By manipulating the deposition conditions, nanocrystalline, microcrystalline and epitaxial (001) 3C-SiC films are obtained with varied properties. The epitaxial 3C-SiC film presents the lowest double-layer capacitance and the highest reversibility of redox probes, because of its perfect (001) orientation and high phase purity. The highest double-layer capacitance and the lowest reversibility of redox probes have been realized on the nanocrystalline 3C-SiC film. Those are ascribed to its high amount of grain boundaries, amorphous phases and large diversity in its crystal size. Based on their diverse properties, the electrochemical performances of 3C-SiC films are evaluated in two kinds of potential applications, namely an electrochemical capacitor using a nanocrystalline film and an electrochemical dopamine sensor using the epitaxial 3C-SiC film. The nanocrystalline 3C-SiC film shows not only a high double layer capacitance (43–70 μF/cm²) but also a long-term stability of its capacitance. The epitaxial 3C-SiC film shows a low detection limit toward dopamine, which is one to 2 orders of magnitude lower than its normal concentration in tissue. Therefore, 3C-SiC film is a novel but designable material for different emerging electrochemical applications such as energy storage, biomedical/chemical sensors, environmental pollutant detectors, and so on

Graphene/3C-SiC Hybrid Nanolaminate

In this work, we demonstrate a one-step approach to create graphene/3C-SiC nanolaminate structure using microwave plasma chemical vapor deposition technique. Layer-by-layer arrangement of thin 3C-SiC layers and graphene sheets is obtained with the thicknesses of the individual 3C-SiC layers and graphene sheets being 5–10 nm and 2–5 nm, respectively. An intimate contact between 3C-SiC and the graphene sheets is achieved and the nanolaminate film shows a high room temperature conductivity of 96.1 S/cm. A dedicated structural analysis of the nanolaminates by means of high-resolution transmission electron microscopy (HRTEM) reveals that the growth of the nanolaminates follows an iterative process: preferential graphene nucleation around the planar defects at the central region of the SiC layer, leading to the “splitting” of the SiC layer; and the thickening of the SiC layer after being “split”. A growth mechanism based on both kinetics and thermodynamics is proposed. Following the proposed mechanism, it is possible to control the layer thickness of the graphene/3C-SiC hybrid nanolaminate by manipulating the carbon concentration in the gas phase, which is further experimentally verified. The high electrical conductivity, large surface area porous structure, feasible integration on different substrates (metal, Mo; semiconductor, Si and 2H-SiC; insulator, diamond) of the graphene/3C-SiC hybrid nanolaminate as well as other unprecedented advantages of the nanolaminate structure make it very promising for applications in mechanical, energy, and sensor-related areas

Image_10_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.jpeg

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_8_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

DataSheet_1_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.xlsx

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_9_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_5_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_3_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_6_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p

Image_2_Unveiling the key genes, environmental toxins, and drug exposures in modulating the severity of ulcerative colitis: a comprehensive analysis.png

BackgroundAs yet, the genetic abnormalities involved in the exacerbation of Ulcerative colitis (UC) have not been adequately explored based on bioinformatic methods.Materials and methodsThe gene microarray data and clinical information were downloaded from Gene Expression Omnibus (GEO) repository. The scale-free gene co-expression networks were constructed by R package “WGCNA”. Gene enrichment analysis was performed via Metascape database. Differential expression analysis was performed using “Limma” R package. The “randomForest” packages in R was used to construct the random forest model. Unsupervised clustering analysis performed by “ConsensusClusterPlus”R package was utilized to identify different subtypes of UC patients. Heat map was established using the R package “pheatmap”. Diagnostic parameter capability was evaluated by ROC curve. The”XSum”packages in R was used to screen out small-molecule drugs for the exacerbation of UC based on cMap database. Molecular docking was performed with Schrodinger molecular docking software.ResultsVia WGCNA, a total 77 high Mayo score-associated genes specific in UC were identified. Subsequently, the 9 gene signatures of the exacerbation of UC was screened out by random forest algorithm and Limma analysis, including BGN,CHST15,CYYR1,GPR137B,GPR4,ITGA5,LILRB1,SLFN11 and ST3GAL2. The ROC curve suggested good predictive performance of the signatures for exacerbation of UC in both the training set and the validation set. We generated a novel genotyping scheme based on the 9 signatures. The percentage of patients achieved remission after 4 weeks intravenous corticosteroids (CS-IV) treatment was higher in cluster C1 than that in cluster C2 (54% vs. 27%, Chi-square test, p=0.02). Energy metabolism-associated signaling pathways were significantly up-regulated in cluster C1, including the oxidative phosphorylation, pentose and glucuronate interconversions and citrate cycle TCA cycle pathways. The cluster C2 had a significant higher level of CD4+ T cells. The”XSum”algorithm revealed that Exisulind has a therapeutic potential for UC. Exisulind showed a good binding affinity for GPR4, ST3GAL2 and LILRB1 protein with the docking glide scores of –7.400 kcal/mol, –7.191 kcal/mol and –6.721 kcal/mol, respectively.We also provided a comprehensive review of the environmental toxins and drug exposures that potentially impact the progression of UC.ConclusionUsing WGCNA and random forest algorithm, we identified 9 gene signatures of the exacerbation of UC. A novel genotyping scheme was constructed to predict the severity of UC and screen UC patients suitable for CS-IV treatment. Subsequently, we identified a small molecule drug (Exisulind) with potential therapeutic effects for UC. Thus, our study provided new ideas and materials for the personalized clinical treatment plans for patients with UC.</p