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The global hazardous waste management market is expected to reach USD 987.51 million by 2027 at a CAGR of 14.48%. The early detection of corrosive, flammable, and infectious toxicants from natural sources or manmade contaminants from different environments is crucial to ensure the safety and security of the global living system. Even though the emergence of advanced science and technology continuously offers a more comfortable lifestyle, there are two sides of the coin in terms of opportunities and challenges, demanding solutions for greener applications and waste-to-wealth strategies. A modern analytical technique based on an electrochemical approach and microfluidics is one such emerging advanced solution for the early and effective detection of toxicants. This review attempts to highlight the different studies performed in the field of toxicant analysis, especially the fusion of electrochemistry and lab-chip model systems, promising for point-of-need analysis. The contents of this report are organised by classifying the types of toxicants and trends in electrochemical-integrated lab-chip assays that test for heavy-metal ions, food-borne pathogens, pesticides, physiological reactive oxygen/nitrogen species, and microbial metabolites. Future demands in toxicant analysis and possible suggestions in the field of microanalysis-mediated electrochemical (bio)sensing are summarised.

In this study, three commercially available low‐density polyethylene (LDPE) polymers produced via a tubular reactor process, with varying melt flow rates at 190°C/2.16 kg (4.0, 1.9, and 0.75 g/10 min), have been selected and subjected to high temperature‐size exclusion chromatography (SEC) analysis coupled with an infrared‐5 (IR‐5), viscometer (VISCO), and multiangle laser light‐scattering detectors. The molecular weight (MW), MW distribution, short‐chain branching (SCB), and long‐chain branching parameters were investigated. It was found that MW obtained by the universal technique (∼1.57–1.7 times) and multiangle laser light‐scattering detection technique is (∼1.43–1.55 times) higher than that of the conventional calibration technique, which could be attributed to structural complexity associated with LDPEs which is not clearly understood by conventional SEC mode alone. The bulk SCB per 1000 total carbon atoms estimated by IR‐5 detection was found to range from 16.50 to 17.80. On the other hand, long chain branching frequency per 1000 total carbon atoms obtained by online VISCO and multiangle laser light‐scattering detection ranged from 0.46 to 0.54 and 0.65 to 0.94, respectively. Further, the significance of long chain branching parameters on the polymer processing behavior was studied in correlation with rheological property (Die swell ratio). [ABSTRACT FROM AUTHOR]

In this work, we reported for the first time, a facile and one step synthesis of gold nanoparticles from HAuCl4, employing tetraphenylborate as the reducing agent. The synthesis is not only facile but also yields “dumb-bell-shaped”particles. This shape appears to arise from a possible emulsion of the products of oxidation/decomposition of tetraphenylborate by HAuCl4, surrounding the particle. The size and shape of the AuNPs were characterized by Transmission electron microscopy (TEM) and UV-visible Spectroscopy. Interestingly, the addition of polyvinylpyrrolidone (PVP) during the synthesis was found to enhance the stability of the nanoparticle dispersion. The particles synthesized under these conditions assume “spherical” shape with the appearance of only transverse surface plasmon resonance band. The highlight of the observations is that the gold nanoparticles synthesized using tetraphenylborate as reducing agent and PVP as stabilizer are highly stable in alkaline medium, in contrast to the synthesis wherein borohydride is used as reducing agent. The AuNPs synthesized using tetraphenylborate and PVP show their mercury sensing behavior only in the alkaline medium. The color of the nanoparticle dispersion undergoes distinct color change from pink to blue with the addition of mercury ions. They also show dramatic selectivity to mercury ions in presence of other interfering ions, Pb2+, Zn2+ and Ca2+.

Aim: To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures., Materials and Methods: Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness., Results: The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image's radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion  for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features., Conclusion: We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.