A Novel MnO<i>_x</i>–MoO<i>_x</i> Codoped Iron-Based Catalyst for NH₃‑SCR with Superior Catalytic Activity over a Wide Temperature Range

MnOx and MoOx, as additives, have proved effective in enhancing the NH3 selective catalytic reduction activity of catalysts. In this study, a new Mn–Fe–Mo catalyst was prepared using both the precipitation method and impregnation method. The redox properties, surface acidity, and reaction intermediates of the prepared catalysts were analyzed using various techniques. The test results showed that the MFMo4 catalyst achieved a NOx conversion rate exceeding 90% over the temperature range of 120–390 °C, demonstrating high potential to replace V-based catalysts due to outstanding activity at both high and low temperatures. The addition of MoOx exerted a detrimental impact on the redox property and avoided the production of N2O, thereby enhancing the N2 selectivity. Furthermore, the codoping of MnOx and MoOx inhibited the adsorption of NOx on the catalysts while resulting in an increased number of Lewis acid sites on the catalyst. This promoted its reaction via the Eley–Rideal mechanism, which played a crucial role in ensuring the catalyst had excellent SCR activity over a wide temperature range

Image_1_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_3_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_2_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Table_1_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.docx

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

Image_4_Single-cell sequencing and establishment of an 8-gene prognostic model for pancreatic cancer patients.tif

BackgroundSingle-cell sequencing (SCS) technologies enable analysis of gene structure and expression data at single-cell resolution. However, SCS analysis in pancreatic cancer remains largely unexplored.MethodsWe downloaded pancreatic cancer SCS data from different databases and applied appropriate dimensionality reduction algorithms. We identified 10 cell types and subsequently screened differentially expressed marker genes of these 10 cell types using FindAllMarkers analysis. Also, we evaluated the tumor immune microenvironment based on ESTIMATE and MCP-counter. Statistical enrichment was evaluated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used all candidate gene sets in KEGG database to perform gene set enrichment analysis. We used LASSO regression to reduce the number of genes in the pancreatic risk model by R package glmnet, followed by rtPCR to validate the expression of the signature genes in different pancreatic cancer cell lines.ResultsWe identified 15 cell subpopulations by dimension reduction and data clustering. We divided the 15 subpopulations into 10 distinct cell types based on marker gene expression. Then, we performed functional enrichment analysis for the 352 marker genes in pancreatic cancer cells. Based on RNA expression data and prognostic information from TCGA and GEO datasets, we identified 42 prognosis-related genes, including 5 protective genes and 37 high-risk genes, which we used to identified two molecular subtypes. C1 subtype was associated with a better prognosis, whereas C2 subtype was associated with a worse prognosis. Moreover, chemokine and chemokine receptor genes were differentially expressed between C1 and C2 subtypes. Functional and pathway enrichment uncovered functional differences between C1 and C2 subtype. We identified eight genes that could serve as potential biomarkers for prognosis prediction in pancreatic cancer patients. These genes were used to establish an 8-gene pancreatic cancer prognostic model.ConclusionsWe established an 8-gene pancreatic cancer prognostic model. This model can meaningfully predict prognosis and treatment response in pancreatic cancer patients.</p

The Quadruplon in a Monolayer Semiconductor

So far, composite particles involving two or three constituent particles have been experimentally identified, such as the Cooper pairs, excitons, and trions in condensed matter physics, or diquarks and mesons in quantum chromodynamics. Although the four-body irreducible entities have long been predicted theoretically in a variety of physical systems alternatively as quadruplons, quadrons, or quartets, the closely related experimental observation so far seems to be restricted to the field of elementary particles (e.g. the recent tetraquark at CERN). In this article, we present the first experimental evidence for the existence of a four-body irreducible entity, the quadruplon, involving two electrons and two holes in a monolayer of Molybdenum Ditelluride. Using the optical pump-probe technique, we discovered a series of new spectral features that are distinct from those of trions and bi-excitons. By solving the four-body Bethe-Salpeter equation in conjunction with the cluster expansion approach, we are able to explain these spectral features in terms of the four-body irreducible cluster or the quadruplons. In contrast to a bi-exciton which consists of two weakly bound excitons, a quadruplon consists of two electrons and two holes without the presence of an exciton. Our results provide experimental evidences of the hitherto theorized four-body entities and thus could impact the understanding of the structure of matter in a wide range of physical systems or new semiconductor technologies

Excitonic Complexes and Optical Gain in Two-Dimensional Molybdenum Ditelluride Well below Mott Transition

Strong Coulomb interaction in 2D materials provides unprecedented opportunities for studying many key issues of condensed matter physics, such as co-existence and mutual conversions of excitonic complexes, fundamental optical processes associated with their conversions, and their roles in the celebrated Mott transition. Recent lasing demonstrations in 2D materials raise important questions about the existence and origin of optical gain and possible roles of excitonic complexes. While lasing occurred at extremely low densities dominated by various excitonic complexes, optical gain was observed in the only experiment at densities several orders of magnitude higher, exceeding the Mott density. Here, we report a new gain mechanism involving charged excitons or trions well below the Mott density in 2D molybdenum ditelluride. Our combined experimental and modeling study not only reveals the complex interplays of excitonic complexes well below the Mott transition, but also provides foundation for lasing at extremely low excitation levels, important for future energy efficient photonic devices

Self-Assembled Cobalt–Nickel Bimetallic-Organic Framework Materials with High Supercapacitor Performance

Two new metal–organic frameworks (MOFs), [Co(bcpp)(bbip)]·H2O (Co-MOF) and [Ni(bcpp)(bbip)]·H2O (Ni-MOF), have been generated based on a V-type flexible carboxylic ligand 3,5-bis(4-carboxyl phenoxy) pyridine (H2bcpp) and a rigid N-donor ligand 1,1′-(1,4-phenylene)bis(1H-benzimidazole) (bbip) by a solvothermal method. Co-MOF and Ni-MOF are isostructural with a 2-fold interpenetrated layered structure. Moreover, a series of bimetallic CoxNiy-MOFs (x/y = 1:1, 2.5:1, 2.75:1, 3:1, 3.25:1, and 3.5:1) were obtained by using one-pot synthesis. Owing to their mixed metallic components and internal layered structure, the bimetallic CoxNiy-MOFs possess a remarkable electrochemical storage property. Significantly, the Co2.75Ni1-MOF has high specific capacitance (699 F g–1) at 0.5 A g–1 and good cycling durability (retained 72.7% over 3100 turns). Additionally, an asymmetrical ultra-capacitor based on Co2.75Ni1-MOF and activated carbon (AC) delivers a maximum energy density of 20.44 Wh kg–1 at 387.49 W kg–1 and a high cycle-to-cycle stability with 85.4% of the primary capacitance over 15,000 turns

Image1_A Prognostic Model of Pancreatic Cancer Based on Ferroptosis-Related Genes to Determine Its Immune Landscape and Underlying Mechanisms.TIF

Pancreatic cancer is one of the malignant tumors with the worst prognosis in the world. As a new way of programmed cell death, ferroptosis has been proven to have potential in tumor therapy. In this study, we used the TCGA-PAAD cohort combined with the previously reported 60 ferroptosis-related genes to construct and validate the prognosis model and in-depth analysis of the differences in the function and immune characteristics of different RiskTypes. The results showed that the six-gene signature prognostic model that we constructed has good stability and effectiveness. Further analysis showed that the upregulated genes in the high-risk group were mainly enriched in extracellular matrix receptor-related pathways and other tumor-related pathways and the infiltration of immune cells, such as B, T, and NK cells, was suppressed. In short, our model shows good stability and effectiveness. Further studies have found that the prognostic differences between different RiskTypes may be due to the changes in the ECM-receptor pathway and activation of the immune system. Additionally, ICI drugs can treat pancreatic cancer in high-risk groups.</p