Resistive Chemosensors for the Detection of CO Based on Conducting Polymers and Carbon Nanocomposites: A Review.

Nanocomposite materials have seen increased adoption in a wide range of applications, with toxic gas detection, such as carbon monoxide (CO), being of particular interest for this review. Such sensors are usually characterized by the presence of CO absorption sites in their structures, with the Langmuir reaction model offering a good description of the reaction mechanism involved in capturing the gas. Among the reviewed sensors, those that combined polymers with carbonaceous materials showed improvements in their analytical parameters such as increased sensitivities, wider dynamic ranges, and faster response times. Moreover, it was observed that the CO reaction mechanism can differ when measured in mixtures with other gases as opposed to when it is detected in isolation, which leads to lower sensitivities to the target gas. To better understand such changes, we offer a complete description of carbon nanostructure-based chemosensors for the detection of CO from the sensing mechanism of each material to the water solution strategies for the composite nanomaterials and the choice of morphology for enhancing a layers' conductivity. Then, a series of state-of-the-art resistive chemosensors that make use of nanocomposite materials is analyzed, with performance being assessed based on their detection range and sensitivity.

The Physicochemical and Antimicrobial Properties of Silver/Gold Nanoparticles Obtained by "Green Synthesis" from Willow Bark and Their Formulations as Potential Innovative Pharmaceutical Substances.

Green chemistry is a pharmaceutical industry tool, which, when implemented correctly, can lead to a minimization in resource consumption and waste. An aqueous extract of Salix alba L. was employed for the efficient and rapid synthesis of silver/gold particle nanostructures via an inexpensive, nontoxic and eco-friendly procedure. The nanoparticles were physicochemically characterized using ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), X-ray diffraction (XRD) and scanning electron microscopy (SEM), with the best stability of up to one year in the solution obtained for silver nanoparticles without any chemical additives. A comparison of the antimicrobial effect of silver/gold nanoparticles and their formulations (hydrogels, ointments, aqueous solutions) showed that both metallic nanoparticles have antibacterial and antibiofilm effects, with silver-based hydrogels having particularly high antibiofilm efficiency. The highest antibacterial and antibiofilm efficacies were obtained against Pseudomonas aeruginosa when using silver nanoparticle hydrogels, with antibiofilm efficacies of over 75% registered. The hydrogels incorporating green nanoparticles displayed a 200% increased bacterial efficiency when compared to the controls and their components. All silver nanoparticle formulations were ecologically obtained by "green synthesis" and were shown to have an antimicrobial effect or potential as keratinocyte-acting pharmaceutical substances for ameliorating infectious psoriasis wounds.

Deep learning applied to hyperspectral endoscopy for online spectral classification.

Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy.

A background correction method to compensate illumination variation in hyperspectral imaging.

Hyperspectral imaging (HSI) can measure both spatial (morphological) and spectral (biochemical) information from biological tissues. While HSI appears promising for biomedical applications, interpretation of hyperspectral images can be challenging when data is acquired in complex biological environments. Variations in surface topology or optical power distribution at the sample, encountered for example during endoscopy, can lead to errors in post-processing of the HSI data, compromising disease diagnostic capabilities. Here, we propose a background correction method to compensate for such variations, which estimates the optical properties of illumination at the target based on the normalised spectral profile of the light source and the measured HSI intensity values at a fixed wavelength where the absorption characteristics of the sample are relatively low (in this case, 800 nm). We demonstrate the feasibility of the proposed method by imaging blood samples, tissue-mimicking phantoms, and ex vivo chicken tissue. Moreover, using synthetic HSI data composed from experimentally measured spectra, we show the proposed method would improve statistical analysis of HSI data. The proposed method could help the implementation of HSI techniques in practical clinical applications, where controlling the illumination pattern and power is difficult.