Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Theoretical-Experimental Study of the Action of Trace Amounts of Formaldehyde, Propionaldehyde, and Butyraldehyde as Inhibitors of the Ziegler-Natta Catalyst and the Synthesis of an Ethylene-Propylene Copolymer.

The copolymer synthesis process can be affected by failures in the production process or by contaminating compounds such as ketones, thiols, and gases, among others. These impurities act as an inhibiting agent of the Ziegler-Natta (ZN) catalyst affecting its productivity and disturbing the polymerization reaction. In this work, the effect of formaldehyde, propionaldehyde, and butyraldehyde on the ZN catalyst and the way in which it affects the final properties of the ethylene-propylene copolymer is presented by analyzing 30 samples with different concentrations of the mentioned aldehydes along with three control samples. It was determined that the presence of formaldehyde 26 ppm, propionaldehyde 65.2 ppm, and butyraldehyde 181.2 ppm considerably affect the productivity levels of the ZN catalyst; this effect increases as the concentration of aldehydes is higher in the process; likewise, these impurities affect the properties of the final product, such as the fluidity index (MFI), thermogravimetric analysis (TGA), bending, tension, and impact, which leads to a polymer with low-quality standards and less resistance to breakage. The computational analysis showed that the complexes formed by formaldehyde, propionaldehyde, and butyraldehyde with the active center of the catalyst are more stable than those obtained by the ethylene-Ti and propylene-Ti complexes, presenting values of -40.5, -47.22, -47.5, -5.2 and -1.3 kcal mol-1 respectively.

Characterization of the Morphological and Chemical Profile of Different Families of Microplastics in Samples of Breathable Air.

Microplastic (MP) contamination has become a problem of great interest to the community at large. The detection of these particles in different ecosystems and foods has been the subject of study. However, the focus of these investigations has been on the identification and quantification of PM by DSC and Pyr-GC/MS and not on how they are transported to reach the air we breathe. In this study, the values of morphological parameters for plastic particles in a range between 1 and 2000 µm, present in the breathable air of 20 neighborhoods in the city of Cartagena, Colombia, were obtained to determine the characteristics that make these particles airborne. The values of parameters were obtained, such as roundness, sphericity, curvature, and the convexity of the particle, as well as its compactness and size, which influence its transport through the air and its ability to be ingested and inhaled. The data obtained in this study allows for simulations and the analysis of the behavior of microplastics once in the environment to predict future settlements. The DSC showed us the melting temperatures of PP, PE, PET, and PS, the Pyr-GC/MS showed the fragmentation patterns, and the presence of these MPs in the samples was confirmed.

Iron Oxide Powder as Responsible for the Generation of Industrial Polypropylene Waste and as a Co-Catalyst for the Pyrolysis of Non-Additive Resins.

For the synthesis of polymeric resins, it is of great importance to review the raw materials and the equipment to be used to avoid the presence of compounds that may affect the effectiveness of the polymerization and the characteristics of the plastic to be obtained. Iron oxide is a compound that can be present in reactors after maintenance due to the techniques used and the cleaning of this equipment, and it can affect the characteristics of the resins, reducing their quality. In this study, the presence of FeO in different concentrations was evaluated to determine its effects on the properties and pyrolysis of polypropylene resins by using X-ray refraction to determine the elements of the samples, evaluating thermal degradation by TGA, the variation in molecular weight by measuring the MFI, and the compounds obtained from pyrolysis by chromatography. The results showed that the thermal degradation decreased as the FeO concentration increased, while for the MFI, the relationship was directly proportional. The evaluation of the compounds obtained from pyrolysis showed an increase in the production of alcohols, alkynes, ketones, and acids, and a decrease in alkanes and alkenes, showing that FeO affects the properties of polypropylene and the compounds that are produced during pyrolysis.

API Content and Blend Uniformity Using Quantum Cascade Laser Spectroscopy Coupled with Multivariate Analysis.

The process analytical technology (PAT) initiative proposed by the US Food and Drug Administration (FDA) suggests innovative methods to better understand pharmaceutical processes. The development of analytical methods that quantify active pharmaceutical ingredients (APIs) in powders and tablets is fundamental to monitoring and controlling a drug product's quality. Analytical methods based on vibrational spectroscopy do not require sample preparation and can be implemented during in-line manufacturing to maintain quality at each stage of operations. In this study, a mid-infrared (MIR) quantum cascade laser (QCL) spectroscopy-based protocol was performed to quantify ibuprofen in formulations of powder blends and tablets. Fourteen blends were prepared with varying concentrations from 0.0% to 21.0% (w/w) API. MIR laser spectra were collected in the spectral range of 990 to 1600 cm-1. Partial least squares (PLS) models were developed to correlate the intensities of vibrational signals with API concentrations in powder blends and tablets. PLS models were evaluated based on the following figures of merit: correlation coefficient (R2), root mean square error of calibration, root mean square error of prediction, root mean square error of cross-validation, and relative standard error of prediction. QCL assisted by multivariate analysis was demonstrated to be accurate and robust for analysis of the content and blend uniformity of pharmaceutical compounds.

Detection of highly energetic materials on non-reflective substrates using quantum cascade laser spectroscopy.

A quantum cascade laser spectrometer was used to obtain the reflection spectra of highly energetic materials (HEMs) deposited on nonideal, low-reflectivity substrates, such as travel-bag fabric (polyester), cardboard, and wood. Various deposition methods were used to prepare the standards and samples in the study. The HEMs used were the nitroaromatic explosive 2,4,6-trinitrotoluene (TNT), the aliphatic nitrate ester pentaerythritol tetranitrate (PETN), and the aliphatic nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). Chemometrics algorithms were applied to analyze the recorded spectra. Partial least squares (PLS) regression analysis was used to find the best correlation between the infrared signals and the surface concentrations of the samples, and PLS combined with discriminant analysis (PLS-DA) was used to discriminate, classify, and identity similarities in the spectral datasets. Several preprocessing steps were applied to prepare the mid-infrared spectra of HEMs deposited on the target substrates. The results demonstrate that the infrared vibrational method described in this study is well suited for the rapid screening analysis of HEMs on low-reflectivity substrates when a supervised model has been previously constructed or when a reference spectrum of the clean substrate can be acquired to be subtracted from the HEM-substrate spectrum.

FT-IR standoff detection of thermally excited emissions of trinitrotoluene (TNT) deposited on aluminum substrates.

A standoff detection system was assembled by coupling a reflecting telescope to a Fourier transform infrared spectrometer equipped with a cryo-cooled mercury cadmium telluride detector and used for detection of solid-phase samples deposited on substrates. Samples of highly energetic materials were deposited on aluminum substrates and detected at several collector-target distances by performing passive-mode, remote, infrared detection measurements on the heated analytes. Aluminum plates were used as support material, and 2,4,6-Trinitrotoluene (TNT) was used as the target. For standoff detection experiments, the samples were placed at different distances (4 to 55 m). Several target surface temperatures were investigated. Partial least squares regression analysis was applied to the analysis of the intensities of the spectra obtained. Overall, standoff detection in passive mode was useful for quantifying TNT deposited on the aluminum plates with high confidence up to target-collector distances of 55 m.