A novel GO hoisted SnO2-BiOBr bifunctional catalyst for the remediation of organic dyes under illumination by visible light and electrocatalytic water splitting.

It is imperative to develop affordable multi-functional catalysts based on transition metals for various applications, such as dye degradation or the production of green energy. For the first time, we propose a simple chemical bath method to create a SnO2-BiOBr-rGO heterojunction with remarkable photocatalytic and electrocatalytic activities. After introducing graphene oxide (GO) into the SnO2-BiOBr nanocomposite, the charge separation, electron mobility, surface area, and electrochemical properties were significantly improved. The X-ray diffraction results show the successful integration of GO into the SnO2-BiOBr nanocomposite. Systematic material characterization by scanning and transmission electron microscopy showed that the photocatalysts are composed of uniformly distributed SnO2 nanoparticles (∼11 nm) on the regular nanosheets of BiOBr (∼94 nm) and rGO. The SnO2-BiOBr-rGO photocatalyst has outstanding photocatalytic activity when it comes to reducing a variety of organic dyes like rhodamine B (RhB) and methylene blue (MB). Within 90 minutes of visible light illumination, degradation of a maximum of 99% for MB and 99.8% for RhB was noted. The oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) performance was also tested for the ternary nanocomposite, and significantly lower overpotential values of 0.34 and -0.11 V (vs. RHE) at 10 mA cm-2 were observed for the OER and HER, respectively. Furthermore, the Tafel slope values are 34 and 39 mV dec-1 for the OER and HER, respectively. The catalytic degradation of dyes with visible light and efficient OER and HER performance offer this work a broad spectrum of potential applications.

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer.

Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.

Solvent-Free Synthesis of S,N-Doped Carbon Dots for Extended Visible-Light-Induced Oxidase-Mimicking Activities and Antimicrobial Applications.

Photo-oxidase nanozymes are emerging enzyme-mimicking materials that produce reactive oxygen species (ROS) upon light illumination and subsequently catalyze the oxidation of the substrate. Carbon dots are promising photo-oxidase nanozymes due to their biocompatibility and straightforward synthesis. Carbon dot-based photo-oxidase nanozymes become active for ROS generation under UV or blue light illumination. In this work, sulfur and nitrogen doped carbon dots (S,N-CDs) were synthesized by solvent-free, microwave assisted technique. We demonstrated that sulfur, nitrogen doping of carbon dots (band gap of 2.11 eV) has enabled photo-oxidation of 3,3,5,5'-tetramethylbenzidine (TMB) with extended visible light (up to 525 nm) excitation at pH 4. The photo-oxidase activities by S,N-CDs produce Michaelis-Menten constant (Km ) of 1.18 mM and the maximum initial velocity (Vmax ) as 4.66×10-8  Ms-1 , under 525 nm illumination. Furthermore, visible light illumination can also induce bactericidal activities with growth inhibition of Escherichia coli (E. coli). These results demonstrate that S,N-CDs can increase intracellular ROS in the presence of LED light illumination.

Micro/Nano fabricated cantilever based biosensor platform: A review and recent progress.

Recent trends in biosensing research have motivated scientists and research professionals to investigate the development of miniaturized bioanalytical devices to make them portable, label-free and smaller in size. The performance of the cantilever-based devices which is one of the very important domains of sensitive field level detection has improved significantly with the development of new micro/nanofabrication technologies and surface functionalization techniques. The cantilevers have scaled down to Nano from micro-level and have become exceptionally sensitive and also have some anomalous associated properties due to the scale. In this review we have discussed about fundamental principles of cantilever operation, detection methods, and previous, present and future approaches of study through cantilever-based sensing platform. Other than that, we have also discussed the past major bio-sensing efforts through micro/nano cantilevers and about recent progress in the field.

Poly-L-Lysine functionalised MWCNT-rGO nanosheets based 3-d hybrid structure for femtomolar level cholesterol detection using cantilever based sensing platform.

In this work we have developed a novel rGO-MWCNT (reduced graphene oxide-multiwalled carbon nanotube) nanocomposite material with Poly-L-Lysine functionalization which can be used for detection of biomolecules with enhanced sensitivity. The reduced GO sheets are found to play a major role as a connector and helps in the assembly of bundles of carbon nanotubes (CNTs) which may sometime play a role of upstanding nanostructures. The overall composite structure is further fully functionalized resulting in an overall high density of amino groups that can be used to capture biomolecules. The sensitivity of the as synthesized film is tested by the oxidation of cholesterol through cholesterol oxidase enzyme that is biochemically immobilized over these composite films. The test for the immobilization density of the novel films are carried out by mounting these films on sensitive thin section static micro/nano-cantilever platforms. The platforms have capability to measure cholesterol traces in blood upto an extent of 100 femto molar through deflection /bending of the cantilevers due to surface reaction. The films developed show a promise of high immobilization density which is further confirmed through fluorescence studies using FITC labeling of functionalized MWCNT-PLL and rGO-PLL films respectively.