Wind-Driven Radial-Engine-Shaped Triboelectric Nanogenerators for Self-Powered Absorption and Degradation of NO_X

As one of the major air pollutants, NOX is rather challenging to remove. The main treatment method is catalytic reduction with plenty of reducing agents, which lacks any effective control in an open air environment such as urban spaces. It is necessary to seek a self-powered electrochemical process for environmental treatment. The triboelectric nanogenerator (TENG), a developing technology with various advantages, is widely used in energy and environmental monitoring and cleaning. In this work, a radial-engine-shaped TENG system with five stacked TENGs is designed to synchronously absorb NOX and degrade its main enrichment forms of nitrate and nitrite in aqueous solution. In addition, the system possesses inherent phase differences and outputs continuous direct current after rectification. Moreover, we demonstrated that, driven by artificial wind at a speed of 6 m/s, the NOX generated by a chemical method was effectively degraded by the radial-engine-shaped TENG system

Wind-Driven Radial-Engine-Shaped Triboelectric Nanogenerators for Self-Powered Absorption and Degradation of NO_X

As one of the major air pollutants, NOX is rather challenging to remove. The main treatment method is catalytic reduction with plenty of reducing agents, which lacks any effective control in an open air environment such as urban spaces. It is necessary to seek a self-powered electrochemical process for environmental treatment. The triboelectric nanogenerator (TENG), a developing technology with various advantages, is widely used in energy and environmental monitoring and cleaning. In this work, a radial-engine-shaped TENG system with five stacked TENGs is designed to synchronously absorb NOX and degrade its main enrichment forms of nitrate and nitrite in aqueous solution. In addition, the system possesses inherent phase differences and outputs continuous direct current after rectification. Moreover, we demonstrated that, driven by artificial wind at a speed of 6 m/s, the NOX generated by a chemical method was effectively degraded by the radial-engine-shaped TENG system

Enhanced Photocatalytic Degradation Performance by Fluid-Induced Piezoelectric Field

The introduction of a piezoelectric field has been proven a promising method to enhance photocatalytic activity by preventing photoelectron–hole recombination. However, the formation of a piezoelectric field requires additional mechanical force or high-frequency ultrasonic baths, which limits its potential application on industrial scale. Therefore, it is of great practical significance to design the catalyst that can harvest the discrete energy such as the fluid mechanical energy to form the electric field. Herein, PZT/TiO₂ catalyst with a core–shell configuration was prepared by a simple coating method. By collecting the mechanical energy of water, an internal piezoelectric field was induced. Under 800 rpm stirring, transient photocurrent measured on PZT/TiO₂ electrode is about 1.7 times higher than that of 400 rpm. Correspondingly, the photocatalytic degradation rate and mineralization efficiency of RhB, BPA, phenol, <i>p</i>-chlorophenol much improved, showing the promoting effect of piezoelectric field generated directly from harvesting the discrete fluid mechanical energy

Multi-parameter Inputted Logic-Gating on Aptamer-Encoded Extracellular Vesicles for Colorectal Cancer Diagnosis

Extracellular vesicles (EVs) have emerged as a potential biomarker in liquid biopsy. However, cancer heterogeneity poses significant challenge to precise molecular diagnosis based on single-parameter input. Hence, strategies for analyzing multiple inputs with molecular computing were developed with the aim of improving diagnostic accuracy in liquid biopsy. In the present study, based on the surface of aptamer-encoded EVs, three toe-hold extended DNA aptamers served as specific inputs to perform AND-logic-gating to distinguish between healthy and cancerous EVs. In addition, this strategy has been successfully employed to analyze circulating EVs in clinical samples from colorectal cancer patients and healthy donors. The developed method has a promising future in the analysis of multiplex EV membrane proteins and the identification of early cancer

Development and validation of a practical machine learning model to predict sepsis after liver transplantation

Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology. Data of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study. After excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all p  Our study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure. Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT).Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis.The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT in our study, which could assist in the clinical decision-making procedure. Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis. The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT in our study, which could assist in the clinical decision-making procedure.</p

Enhancing Specific Fluorescence In Situ Hybridization with Quantum Dots for Single-Molecule RNA Imaging in Formalin-Fixed Paraffin-Embedded Tumor Tissues

Single-molecule fluorescence in situ hybridization (smFISH) represents a promising approach for the quantitative analysis of nucleic acid biomarkers in clinical tissue samples. However, low signal intensity and high background noise are complications that arise from diagnostic pathology when performed with smFISH-based RNA imaging in formalin-fixed paraffin-embedded (FFPE) tissue specimens. Moreover, the associated complex procedures can produce uncertain results and poor image quality. Herein, by combining the high specificity of split DNA probes with the high signal readout of ZnCdSe/ZnS quantum dot (QD) labeling, we introduce QD split-FISH, a high-brightness smFISH technology, to quantify the expression of mRNA in both cell lines and clinical FFPE tissue samples of breast cancer and lung squamous carcinoma. Owing to its high signal-to-noise ratio, QD split-FISH is a fast, inexpensive, and sensitive method for quantifying mRNA expression in FFPE tumor tissues, making it suitable for biomarker imaging and diagnostic pathology