A novel and effective strategy for preparing polyoxometalate-coupled MXene nanohybridsviapoly(ionic liquid) linkers is presented for enhanced supercapacitive performance.

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Droplet bouncing on repellent solid surfaces (e.g., the lotus leaf effect) is a common phenomenon that has aroused interest in various fields. However, the scenario of a droplet bouncing off another droplet (either identical or distinct chemical composition) while moving on a solid material (i.e., ricocheting droplets, droplet billiards) is scarcely investigated, despite it having fundamental implications in applications including self‐cleaning, fluid transport, and heat and mass transfer. Here, the dynamics of bouncing collisions between liquid droplets are investigated using a friction‐free platform that ensures ultrahigh locomotion for a wide range of probing liquids. A general prediction on bouncing droplet–droplet contact time is elucidated and bouncing droplet–droplet collision is demonstrated to be an extreme case of droplet bouncing on surfaces. Moreover, the maximum deformation and contact time are highly dependent on the position where the collision occurs (i.e., head‐on or off‐center collisions), which can now be predicted using parameters (i.e., effective velocity, effective diameter) through the concept of an effective interaction region. The results have potential applications in fields ranging from microfluidics to repellent coatings.

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Fabrication of a graphene-based gold nanocomposite using PVEIM-b-PNIPAM as a stabilizer and its thermosensitivity for the reduction of 4-nitrophenol.

Biofilm detachment often has detrimental effects such as pipe obstruction and infection, yet the detachment mechanisms underlying dispersal remain largely unknown. In this study, a stress response mechanism known as glutathione‐gated potassium efflux (GGKE) was evaluated as an active detachment mechanism in the dispersal of Pseudomonas aeruginosa biofilms. N‐ethylmaleimide (NEM) was used to activate potassium efflux proteins (Kef) associated with the GGKE pathway. This stress response mechanism was hypothesized to lead to altered cation concentration, which can potentially affect polymer bridging in biofilms, and ultimately cause biofilm detachment. Results showed the activation of GGKE by NEM exposure caused biofilm detachment without inducing a measurable change in viability, and detached biomass concentration and composition were dependent on NEM concentration. More detached biomass was observed with higher concentrations of NEM, with a trend of increasing polymer detachment. The detachment was likely resulting from a weakened biofilm structural integrity induced by bridge denaturing from GGKE activation. This study is important in understanding biofilm detachment from engineered systems such as membrane aerated bioreactors.

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.