Quantification of peracetic acid (PAA) in the H2O2 + acetic acid reaction by the wavelength shift analysis in near-UV/visible absorption region.

In our study, we present an innovative method for the analysis and real-time monitoring of peracetic acid (PAA) formation within the near-UV/Vis (visible) wavelength region. PAA's absorption spectrum, influenced by its presence in a complex quaternary equilibrium mixture with hydrogen peroxide (H2O2), acetic acid, and water, lacks discernible peaks. This inherent complexity challenges conventional analytical techniques like Beer's law, which rely on absorption intensity as a foundation. To address this challenge, we introduce a novel approach that centers on the analysis of blue shifts in absorption wavelengths, particularly at an absorbance of 0.8 a.u. This method significantly enhances the precision of calibration curves for both diluted PAA and H2O2, unveiling an exponential correlation between wavelength and the logarithm of concentration for both components. Significantly, our approach allows for real-time and accurate measurements, especially during the dynamic PAA formation reaction. Our results exhibit excellent agreement with data obtained from Fourier-transform infrared (FT-IR) spectroscopy, validating the reliability of our method. It's noteworthy that under stable PAA concentration conditions (after 12 h of solution interaction), both traditional absorption method and our approach closely align with the FT-IR method. However, in dynamic scenarios (0-12 h), the absorption method exhibits higher error rates compared to our approach. Additionally, the increased concentration of a catalyst, sulfuric acid (H2SO4), significantly reduces the errors in both methods, a finding that warrants further exploration. In summary, our study not only advances our understanding of PAA and its spectral behavior but also introduces innovative and precise methods for determining PAA concentration in complex solutions. These advancements hold the potential to revolutionize the field of chemical analysis and spectroscopy.

A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors.

The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The optimized multiple linear regression model, which was trained and tested on 226 natural compounds demonstrates reliable prediction performance (r 2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors. The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13721-021-00326-2.

Non-invasive and Time-dependent Blood-sugar Monitoring via Breath-derived CO2 Correlation Using Gas Chromatograph with a Milli-whistle Gas Analyzer.

A clear and positive correlation between the CO2 concentration and the blood-sugar level has been observed via a non-invasive and time-dependent monitoring of CO2 concentration from human breath, which is carried out by using a home-made gas chromatography (GC)/milli-whistle compact analyzer. The time-dependent sampling of the CO2 concentration correlated between 5.0 to 5.6% (1% = 104 ppm) in accordance with blood-sugar level variations of 80 to 110 mg/dL. The analytical method results in a rapid, continuous and non-invasive determination of blood-sugar level via measurement of the CO2 concentration exhaled from the lungs.

Stable π-π dependent electron conduction band of TPP[M(Pc)L2]2 molecular conductors (TPP = tetraphenylphosphonium; M = Co, Fe; Pc = phthalocyaninato; L = CN, Cl, Br).

The partially-oxidized TPP[M(Pc)L(2)](2) molecular conductors exhibit variable electronic and magnetic transport bulk materials properties due to central metal and axial ligand molecular modifications. The controllable electrical conductivity and giant negative magnetoresistance can be mainly attributable to the varying ligand field energy and physical bulkiness of the axial ligands which cause modulation in the intra-molecular π-d (Pc-M) and inter-molecular π-π (Pc-Pc) interactions in the TPP[M(Pc)L(2)](2) system, respectively. Characterization of the electronic conduction band utilizing one-dimensional (1-D) tight-binding approximation from infrared reflectance and thermoelectric power profile reveal consistent band widths of 0.43 eV-0.62 eV for the Co series (L = Br < Cl < CN) and 0.44-0.56 eV for the Fe series (L = Br < Cl < CN). The fixed band width suggests that stable electron conduction bands (transport pathway) can be constructed which can withstand the molecular π-d interaction modifications that severely alter the bulk electronic and magnetic materials properties of the TPP[M(Pc)L(2)](2) molecular conductors.