Development of a bioreactor with an integrated non-dispersive infrared CO sensor for rapid and sensitive detection of Cr(VI) toxicity in water.

Whole-cell bioreactors equipped with external physico-chemical sensors have gained attention for real-time toxicity monitoring. However, deploying these systems in practice is challenging due to potential interference from unknown wastewater constituents with liquid-contacted sensors. In this study, a novel approach using a bioreactor integrated with a non-dispersive infrared CO₂ sensor for both toxicity detection and real-time monitoring of microbial growth phases was successfully demonstrated.

Chiral plasmonic Au@Pt nanoparticles for detection of HO and Hg and enantiomeric differentiation.

Chiral Au@Pt nanoparticles (NPs) with optically plasmonic and catalytic active surfaces were sustainably prepared to serve as label-free surface enhanced Raman scattering (SERS) platform to distinguish D- and L-enantiomers of alanine and tartaric acid. Surface morphologies were characterized by high-angle annular dark-field imaging-scanning transmission electron microscopy (HADDF-STEM) and selected area energy diffraction (SAED) patterns. The amounts of Pt on chiral Au NPs were estimated by the inductively coupled plasma-optical emission spectrometer (ICP-OES) and X-ray diffraction (XRD).

An Oxazole Alkaloid, Terpenoids, and Cyclodipeptides With Cytotoxic and Nitric Oxide Inhibitory Effects From a Mangrove-Derived Fungus Trichoderma sp. GXT-22.1.

Chemical investigation of the mangrove-derived fungus Trichoderma sp. GXT-22.1 led to the isolation and identification of 10 secondary metabolites, including one new compound, 5'-(4-methoxyphenyl)-1',3'-oxazole (1), one new natural compound, (E)-6,10-dimethyl-5-undecene-2,9,10-triol (2), along with eight known compounds, tricholumin A (3), harzianol J (4), cyclonerodiol (5), 10,11-dihydro-11-hydroxycyclonerodiol (6), cyclonerodiol B (7), epicyclonerodiol oxide (8), cyclo(Val-Pro) (9), and cyclo-(4-hydroxyprolinyl-leucine) (10).

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance.

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass production. The monitoring of parts quality directly during manufacturing promises to solve this problem, while machine learning showed efficient performance correlating versatile manufacturing measurements with different quality grades.

Classification of glucose-level in deionized water using machine learning models and data pre-processing technique.

Accurate monitoring of glucose levels is essential in the field of diabetes detection and prevention to ensure appropriate treatment planning. Conventional blood glucose monitoring methods, although widely used, are intrusive and frequently result in discomfort. This study investigates the use of Raman spectroscopy as a non-invasive method for estimating glucose concentrations.