Chemical Analysis Methods Research Articles

Ecological indexes in analytical chemistry

The development of environmentally safe methods of analytical chemistry has been one of the dominant directions of scientific research in recent decades. Ecological indexes have become a valuable tool for assessing and quantitatively determining the impact of conducting chemical analysis on the environment. This review article discusses the main ecological indexes presented in the literature, including aspects such as the safety of chemical reagents used, analysis productivity, energy consumption, and waste generation. The review reflects the latest achievements in the field of “green” indexes and their potential role in transitioning to more ecological and sustainable analytical methods.

ИСПОЛЬЗОВАНИЕ ПОЛИМЕР-КОЛЛОИДНЫХ КОМПОЗИЦИЙ НА ОСНОВЕ ХИТОЗАНА ДЛЯ ОЧИСТКИ СТОЧНЫХ ВОД ОТ ВЕЩЕСТВ БЕЛОК-ЛИПИДНОЙ ПРИРОДЫ

The paper studies the use of polymer-colloidal compositions based on chitosan with sodium dodecyl sulfate in the processes of purification of protein- and fat-containing wastewater. Using spectrophotometry, high (93-99 %) degrees of clarification were determined under conditions of model wastewater, both with a high protein content and with a high fat content. Using quantitative chemical analysis methods, the COD parameter and fat content in wastewater were determined and it was shown that already in the concentration range from 1 to 50 mg/l for the chitosan-surfactant composition of the working composition, permissible standardized values are achieved - fats are completely removed, and the content of organic impurities decreases by 2-4 times compared to the original model wastewater.

A novel approach for quantitative determination of lignin content in tobacco via multiCP/MAS NMR spectroscopy

AbstractAs the main structural component of tobacco cell wall, lignin content is an important factor affecting the safety of tobacco smoking. However, it is time‐consuming to quantify lignin by conventional wet chemical analysis methods. In this work, a 13C multiCP/MAS NMR spectral analysis method for tobacco lignin was established. The multiCP/MAS NMR sequence was optimized for tobacco lignin. The optimized nuclear magnetic sequence parameters were 9 CP cycles of 1.5 ms, repolarization time of 0.7 s, and a total acquisition time of 130 min. Subsequently, TMSP was used as the internal standard substance to establish the working curve, and the correlation coefficient was 0.9946. The relative standard deviation (RSD, n = 5) was 3.26%. This method was applied to the determination of lignin content in different types of tobacco samples. The relative error in the determination of lignin content by this method did not exceed 4.46% compared to the results of the chemical method. The results showed that the 13C multiCP/MAS NMR spectral analysis method had the advantages of accuracy and rapidity, which provided a new technical means for the quantitative study of tobacco cell wall substances.

Smart E-Tongue Based on Polypyrrole Sensor Array as Tool for Rapid Analysis of Coffees from Different Varieties.

Due to the lucrative coffee market, this product is often subject to adulteration, as inferior or non-coffee materials or varieties are mixed in, negatively affecting its quality. Traditional sensory evaluations by expert tasters and chemical analysis methods, although effective, are time-consuming, costly, and require skilled personnel. The aim of this work was to evaluate the capacity of a smart electronic tongue (e-tongue) based on a polypyrrole sensor array as a tool for the rapid analysis of coffees elaborated from beans of different varieties. The smart e-tongue device was developed with a polypyrrole-based voltammetric sensor array and portable multi-potentiostat operated via smartphone. The sensor array comprised seven electrodes, each doped with distinct counterions to enhance cross-selectivity. The smart e-tongue was tested on five Arabica coffee varieties (Typica, Bourbon, Maragogype, Tabi, and Caturra). The resulting voltammetric signals were analyzed using principal component analysis assisted by neural networks (PCNN) and cluster analysis (CA), enabling clear discrimination among the coffee samples. The results demonstrate that the polypyrrole sensors can generate distinct electrochemical patterns, serving as "fingerprints" for each coffee variety. This study highlights the potential of polypyrrole-based smart e-tongues as a rapid, cost-effective, and portable alternative for coffee quality assessment and adulteration detection, with broader applications in the food and beverage industry.

Predicting total alkalinity in saline water using machine learning: A case study with RapidMiner

This study investigates the use of machine learning models to predict total alkalinity (TA) based on chloride concentration (Cl-), pH and temperature. Utilizing RapidMiner's Auto Mode, six machine learning models were applied to a dataset of 111 water samples from the Nhà Bè River. The models' performances were evaluated using Root Mean Square Error (RMSE) and R² metrics, with the Generalized Linear Model (GLM), Support Vector Machine (SVM) and Deep Learning models identified as the top performers. Correlation and coefficient analyses revealed that Cl- had the most significant impact on TA prediction, followed by temperature and pH. These findings underscore the effectiveness of machine learning in water quality monitoring, presenting a cost-effective alternative to traditional chemical analysis methods.

Crystal structural perfection and the associated properties of quartz as an indicator of vein vertical zoning: Example of the Verkhnealiinskoye gold deposit in Transbaikalia, Russia

Research subject. A section of the V ore zone in the Verkhnealiinskoye gold deposit in Transbaikalia. Materials and methods. Samples of host rocks and ores from the core of exploratory wells, which cut the ore zone at depth intervals of 27–34; 57–104; 153–168; 285–293 m, were studied by the methods of chemical analysis, as well as determination of crystal structural perfection, phase α→β transition temperatures, and electrical resistivity of vein quartz. Results and conclusions. An increase in the degree of crystal structural perfection (CSP) of vein quartz in cross sections in the direction from the quartzification zone at the contacts to the axial parts of the vein from 59 to 73 (average 63) in the upper section, from 54 to 79 (average 67.2) in the lower-middle section, and from 86 to 93 (average 89.2) in the section below the wedging of the conductor was established. Over the entire depth range from 27 to 168 m, the average CSP value degree increases from 63 to 89.2. Similarly, although less clearly, phase (α→β) transition temperatures in quartz undergo changes; their connection with the CSP value was revealed. The electrical resistivity of vein quartz increases during the transition from the quartz zone to the quartz vein due to a decrease in the number of inclusions and impurities of hydromica and chlorite. The possibility of using the СSP value in combination with data on the content of gold, silver, and bismuth to assess the level of an erosion section of newly discovered gold manifestations is confirmed, both in individual undercuts by boreholes and in the form of dumps or bedrock outcrops of quartz veins.

Study on the occurrence characteristics of arsenic in copper matte

The occurrence characteristics of arsenic in matte phase are unclear, which leads to the current treatment technology not being able to remove arsenic from matte phase significantly, thus causing a large amount of arsenic to affect smelting links such as copper converting and electrolytic refining. This paper uses instrumental analysis such as XRD, SEM-EDS, MLA and chemical analysis methods such as chemical phase extraction to comprehensively analyze the occurrence characteristics of arsenic in matte phase. The results show that the occurrence states of arsenic in matte are mainly arsenic sulfide, arsenic oxide, arsenate and residual arsenic. Most of the arsenic is in residual state in matte; arsenic in matte is extremely stable and difficult to exchange with the outside world; in high-grade matte, the arsenic content is relatively high. The main occurrence state of arsenic is copper-based complex alloy compounds, in which the arsenic content is about 1-10%. Since the metal bond between Cu-As is extremely stable, it is difficult to separate arsenic from it, which is also the key to the difficulty in completely removing arsenic from matte.

Quantifying pigment features of Thangka Five Buddhas using hyperspectral imaging

Thangka, a masterpiece of Tibetan painting, is renowned for its adept use of natural mineral pigments such as gold, turquoise, and cinnabar, which imbue it with profound artistic and historical significance. Presently, chemical analysis methods relying on microscopic perspectives are prevalent in researching the pigment components of cultural artifacts. However, these methods suffer from quantization gaps and carry the risk of damaging the relics. Hence, in this study, we focus on Thangka Five Buddha as our experimental sample and propose a novel approach utilizing Linear Spectral Mixed Analysis (LSMA) based on Hyperspectral Imaging (HSI) technology to perform quantitative analysis of pigment components at a sub-pixel level. The results indicate the following. 1) A database of spectral curves for 25 representative Thangka pigments was established, covering 196 bands from 393 to 800 nm VIR-NIR range. 2) The LSMA model successfully separated the 13 pigment components of the Thangka at the sub-pixel level, achieving a Root Mean Square Error (RMSE) of 0.0186, which indicates high classification accuracy. 3) The quantitative analysis reveals that 33.07 % of the area is painted using a single pigment, while 56.01 % is painted using a combination of two pigments. Verdigris (18.56 %), malachite (17.52 %), and cinnabar (10.91 %) are the pigment types with the highest proportions among them. Out of the 521 pigment combinations, verdigris and turquoise (4.55 %), malachite and calcite (4.02 %), minium and cinnabar (2.87 %), and turquoise and malachite (2.82 %) are more commonly used. 4) The application of quantitative analysis methods demonstrates significant potential in painting techniques, authentication processes, and establishing historical dating, among other areas of study.

Effect of Cr on nano-indentation Young's modulus and hardness of the β-Ti phase in equilibrium with α2-Ti3Al and γ-TiAl phases in Ti-Al-Cr alloys

Young's modulus and hardness of the constituent phases of β-Ti (bcc or B2), in equilibrium with α2-Ti3Al (D019) and γ-TiAl (L10) phases in Ti-Al-Cr ternary alloys were quantitatively evaluated in terms of the chemical composition analysis and nano-indentation method, in order to identify the Cr concentration dependence on the mechanical properties of the β phase. An alloy with a nominal composition of Ti-43Al-3Cr (at.%) decomposes into the three phases of β, α2, and γ by equilibrium heat treatment at 1373 K, with chemical composition of Ti-37.2Al-5.2Cr, Ti-38.9Al-2.2Cr, and Ti-47.4Al-1.4Cr, respectively. The β phase shows a Young's modulus of 141 ± 5 GPa, smaller and less orientation dependent than the other two phases of α2 (174 ± 9 GPa) and γ phases (164 ± 15 GPa). In contrast, the β phase has a hardness of 5.9 ± 0.3 GPa, slightly softer than the α2 phase (6.4 ± 0.8 GPa) but much harder than the γ phase (3.7 ± 0.6 GPa). The alloys Ti-44Al-4Cr and Ti-45Al-6Cr consist of two phases of β and γ phases, and the chemical composition of the β phase in these two alloys is Ti-36.5 Al-8.5 Cr and Ti-36.3 Al-13.9 Cr, respectively, with nearly the same Al concentration. The Young's modulus and hardness of the β phase with the latter composition become 158 GPa and 6.7 GPa, respectively, with increasing Cr content. These results found that the effect of solid solution Cr on the Young's modulus for β phase is relatively weak, and that on the hardness is strong, in comparison with that for the other two phases.

Metabolic Aspects of Chronic Prostatitis/Chronic Pelvic Pain Syndrome

Symptoms of prostatitis, in particular, pain and urination disorders significantly affect the quality of life of patients. Metabolic mechanisms of the pathogenesis of prostatitis and the formation of prostate calcifications are of interest for modern research. The objective: to determine the relationship between the concentration of uric acid, oxalates and pH in blood serum, urine and ejaculate with symptoms of prostatitis and ultrasound signs of prostate calcifications. Materials and methods. 102 men aged 18 to 45 were included in the study. Group 1 (n=34) consisted of patients with chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). Group 2 included patients with asymptomatic prostatitis (n=34). They had no complaints, but laboratory signs of prostatitis were observed (pyospermia, leukocytes more than 10 per high power field in the ejaculate). The material (urine, ejaculate) of 34 healthy men was used as a control (Group 3). Prostatitis symptoms were assessed using the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) scale, and depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9) scale. All participants underwent ejaculate analysis. Interleukins (IL) 1β and IL-10 were determined by enzyme immunoassay in ejaculate. In all cases, daily diuresis, pH of urine, concentration of uric acid, oxalates in urine and ejaculate were determined by methods of analytical chemistry. Patients of Group 1 received a course of extracorporeal shock wave therapy (ESWT) for 10 sessions, 2-3 times a week. Treatment success was defined as a reduction of 6 points or more on the NIH-CPSI total score. After receiving a course of ESWT, patients of Group 1 were recommended to follow a diet for 2 months (± 2 weeks), after which they were subject to re-examination. All other study participants (Group 2 and Group 3) followed the diet immediately after the initial examination. After 2 months (± 2 weeks), after which they were also subject to re-examination. Results. Both high and low values of the studied indicators were found in all patients. The analysis of the obtained data on the concentration of uric acid and oxalates in the urine, blood serum and ejaculate of the study participants showed no statistically significant difference between the groups. The concentration of uric acid in blood serum was inversely correlated with its concentration in urine and ejaculate (r=–0.238, p=0.016; r=–0.267, p=0.007, respectively). In addition, it was inversely correlated with the concentration of oxalates in urine, ejaculate and their daily excretion (r=–0.301, p=0.02; r=–0.288, p=0.003; r=–0.212, p=0.032, respectively). The ejaculate pH was correlated with the concentration of uric acid in sperm and blood serum (r=0.297, p=0.002; r=–0.467, p=0.001, respectively), as well as with the presence of flickering artifact during ultrasonography of the prostate gland (r=–0.249, p=0.011). In turn, the urine pH level was positively correlated with the presence of a flickering artifact and the assessment of depression symptoms according to the PHQ-9 questionnaire (r=0.208, р=0.036; r=–0.199, р=0.045, respectively). Correlations between metabolic indicators and evaluation of prostatitis symptoms according to the NIH-CPSI scale were absent. As a result of adherence to the diet for 2 months, there was no recurrence of the disease or deterioration of the condition of patients in Group 1, as well as the appearance of prostatitis symptoms in the participants of Groups 2 and 3. This may also have been facilitated by compliance with our general recommendations for maintaining a healthy lifestyle. Significant changes in the studied metabolic indicators were observed in all studied groups. In Group 1, the activity of prostatitis symptoms and depression decreased statistically significantly as a result of using the diet. The average difference in the total score of the NIH-CPSI questionnaire was 4.2±6.8, p<0.001; the total score of the PHQ-9 questionnaire was 4.1±2.9, p<0.001. Conclusions. The results of the study show that the concentration of uric acid, oxalates in urine, semen, and blood serum, as well as the pH values of urine and ejaculate, most of which traditionally characterize stone formation in the urinary tract, significantly affect the condition of patients with chronic calculous prostatitis. Adherence to the diet allows patients to influence the indicators of salt transport, reduce the activity of symptoms and the likelihood of disease recurrence. Along with this, no significant relationship was found between the studied indicators and the activity of prostatitis symptoms and depression in such patients.

OXIDATION OF 4-METHYLPYRIDINE ON V-Cr-O CATALYST

Isonicotinic acid derivatives are used in the development of new antibacterial agents, active ingredients in pesticides and herbicides. In addition, isonicotinic acid is a precursor for the synthesis of isoniazid, one of the most effective anti-TB drugs. Considering the importance of isoniazid in the treatment of tuberculosis, improving methods of synthesizing isonicotinic acid has a direct impact on the availability and cost of this life-saving drug. The goal of this work was to investigate the efficiency of V-Cr-O catalyst in the oxidation of 4-methylpyridine to produce isonicotinic acid, to study its composition and properties. Research methods. Methods of chemical analysis, scanning electron microscope (SEM), Raman spectroscopy were applied. Results and Discussion. In this work, the processes of isonicotinic acid synthesis by oxidation of 4-methylpyridine on the V-Cr-O catalyst were studied. Analysis of the obtained results showed that with an increase in the water feed, there is a general tendency towards a decrease in the conversion of 4-methylpyridine at all temperatures. For example, at a temperature 300oC, the degree of conversion drops from 91.72% at 1.79 g/h to 84.20% at 6.6 g/h. The yield of isonicotinic acid increases as the amount of water rises, reaching its peak at 47.46% with a temperature of 360°C and a water supply of 6.6 g/h. In contrast, the minimum yield is only 15.27% at 280°C with a water supply of 1.79 g/h. This highlights the positive correlation between water supply and yield, especially at higher temperatures.

Increasing the nutritional value of cupcakes and evaluating their quality

The use of non-traditional types of raw materials in the production of flour confectionery products makes it possible to increase the nutritional value of products and enrich them with useful nutrients (proteins, dietary fiber, vitamins, and minerals). During the study, organoleptic, chemical, microbiological, and biochemical methods of analysis of raw materials, semi-finished products and products were used to determine the chemical composition and calculate the nutritional value of the proposed cupcakes. Partial replacement of premium-grade wheat flour in the recipe for the Stolichny cake with stone fruit flour (pomegranate and apricot kernels) and whole-grain wallpaper wheat flour, as well as the introduction of highly sugared molasses, sunflower oil with the addition of olive oil, contribute to the improvement organoleptic and physicochemical characteristics of products, increase their functionality and do not lead to a deterioration in microbiological characteristics. Consumption of 100 g of developed fortified products ensures the degree of satisfaction of the daily requirement for protein by 19 %, fat – 8, carbohydrates – 14, dietary fiber – 28, minerals – 6–30, vitamins – 6–39 %. The results of the study will contribute to expanding the range of enriched muffins with increased nutritional value using traditional and non-traditional raw materials that ensure food safety and resource conservation.

1H-NMR, HPSEC-RID, and HPAEC-PAD Characterization of Polysaccharides Extracted by Hydrodynamic Cavitation from Apple and Pomegranate By-Products for Their Valorization: A Focus on Pectin

Several chemical analytical methods were applied to characterize the chemical structure of polysaccharides extracted from discarded apples and pomegranate peels using hydrodynamic cavitation methods in a circular economy perspective. In particular, the purity of the polysaccharides and the degrees of acetylation and methylation were evaluated by proton Nuclear Magnetic Resonance (1H-NMR) analysis; simple sugars and galacturonic acid were analyzed simultaneously by High-Performance Anion Exchange Chromatography—Pulsed Amperometric Detector (HPAEC-PAD); the molecular weight of the extracted polysaccharides was determined by High-Performance Size Exclusion Chromatography-Refractive Index Detector (HPSEC-RID). The results showed a negligible presence of co-precipitated proteins/tannins, easily removed by dialysis, as well as other co-precipitated molecules such as monosaccharides and organic acids. Polysaccharides from apples consisted mainly of pectic material with a prevalence of homogalacturonans. Polysaccharides from pomegranate peels showed greater compositional variability with significant amounts of arabinose and galactose, a lower content of pectin, and the presence of rhamnogalacturonans I. Both polysaccharides were highly methylated and differed in the degree of acetylation, which could lead to different properties. Polysaccharides from apples presented two main molecular weights (>805 kDa and 348–805 kDa, respectively), while those from pomegranate peel showed a major fraction at 348 kDa and minor fractions < 23 kDa. In conclusion, the research tools proposed by this study have allowed defining the macrostructure of polysaccharides in a quick and efficient way to valorize these food by-products.

A guidance for the enrichment of micropollutants from wastewater by solid-phase extraction before bioanalytical assessment

BackgroundWastewater can contain a complex mixture of organic micropollutants, with both chemical analysis and effect-based methods needed to identify relevant micropollutants and detect mixture effects. Solid-phase extraction (SPE) is commonly used to enrich micropollutants prior to analysis. While the recovery and stability of individual micropollutants by SPE has been well studied, few studies have optimized SPE for effect-based methods. The aim of the current study was to develop and evaluate two standard operating procedures (SOPs) for the enrichment of micropollutants in preparation for chemical analysis and bioanalysis, one covering a broad range of chemicals and the other selective for estrogenic chemicals.ResultsPristine surface water spiked with > 600 micropollutants was used to develop a generic extraction method for micropollutants with a wide range of physiochemical properties, while water spiked with estrogenic chemicals was used to identify a selective extraction method. Three different SPE sorbents were tested, with recoveries of individual chemicals and effect in assays indicative of mutagenicity, estrogenic activity, and fish embryo toxicity assessed. The sorbent HRX at pH 7 was selected for the generic extraction method as it showed the best recovery of both individual chemicals and effect in the bioassays. The sorbent HLB at pH 3 showed optimal recovery of estrogenic chemicals and estrogenic activity. The two optimal SPE methods were applied to spiked and unspiked wastewater effluents, with the concentrations of detected chemicals and observed effects similar to those of previous studies. The long-term storage of both extracts and SPE cartridges for estrogens and estrogenic activity after extraction with the HRX and HLB methods were evaluated, with estrogenic effectiveness close to 100% after 112 days when HLB was used.ConclusionsHRX is recommended for generic extraction, while HLB is optimal for the selective extraction of estrogenic micropollutants. However, if a laboratory only wants to use a single SPE sorbent, HLB can be used for both generic and selective extraction as it yielded similar chemical and effect recovery as HRX for a wide range of micropollutants. This paper is supplemented by the final SOP that includes a variant for generic extraction and one for the extraction of estrogenic chemicals.

Detection of Oil Mineral Pollution in Tigris River from Aldora Refined using Absorbance Spectroscopy

Accurately identifying the kind and amount of dissolved metal salts in wastewater used in oil refining processes is an iconic feature of ultraviolet and visible absorption spectroscopy. This method relies on the dissolved metal salts' ability to absorb light at certain wavelengths after reacting with it. The experiments were conducted in a lab setting with a broadband source (200-800 nm) to measure the absorbance of dissolved element salts and precisely identify the lowest concentration up to 2 ppm. A mixture of the mineral salts from oil refining operations was prepared and diluted to different concentrations using a standard solution. This allowed us to study and compare this result with the absorbance behavior of the wastewater from the Al-Dora Refinery. The two results reinforced that we can accurately estimate the detection parameters for the lowest water contamination. These materials are lead nitrate (PbNO3), phenol, calcium carbonate (CaCO3), sodium chloride (NaCl2), sulfide (SO4), and nitrate (NO3). At wavelengths of 340, 404, and 741 nm, the concentrations (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100) ppm were found, and for the concentration of 10ppm, the absorbance (0.15323, 0.15326, and 0.14685) was found, respectively. The process that has been tested with varying concentrations is considered and simulates the variation in river water concentrations caused by the river's water level and flow rate changes by the effect of rain abundance and thawing. It is fast, accurate data analysis, and a lower cost compared with the other chemical analysis and conventional methods.

Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic Genomes" from Interatomic Electron Structures.

The description and analysis of chemical bonds have been difficult following the popularization of electronic structure calculations. Although many attempts have been made from the perspective of electronic structure, the sheer volume of information in the electronic structure has left contemporary chemical bond analysis methods grappling with an inescapable "Trilemma" where the model briefness, generality, and descriptiveness (descriptive power) cannot be obtained simultaneously. To push the generality and descriptiveness to their extremes, herein a general machine learning-based framework is introduced to compact chemical bonds into a detailed residue-by-residue "genome" with matched encoding/decoding tools. The framework fuses the quantum mechanical aspects, auto feature extraction, nanostructures and/or simulations, and generative models. The encoded genomes are information-dense and decodable, where 100% generality is guaranteed. The descriptiveness of genomes appears to be broader than most known models. As a proof of concept, the realization presented in this work compacts the complete information regarding two critical chemical bonds in thiolate-protected gold nanoclusters, the S-Au and Au-Au bonds, from a Bosonic-Fermionic character perspective into 8-valued genomes. The machine learning component is trained based on 26,528 density functional theory simulated electron localization function images. With an exploration of the space span for the genome, bond polarization, hybridization, intrusion of other atoms, alignments, crystal orientation, atomic motions, and more details are observed. Furthermore, it has emerged from extensive generation tests that molecules and solids can be integrated in such a concise manner than is typically achieved with purely geometric representations. To showcase the intraclass complexity of S-Au and Au-Au bonds visually, a roadmap is plotted by summarizing and correlating the similarities of 8-value-genomes. Furthermore, genomes can be associated with realistic indices easily with a simple multilayer perception architecture as a simple calculating tool. Besides, there are 3 sets of applications, including a set of chemisorption, a set of molecular dynamical analysis, and a set of ultrafast processes, showcasing the interpretability potentials of interatomic genomes in the geometric structures, kinetic properties, and vibration characteristics of molecular systems. As the framework rose to the challenge of nanoclusters from a complicated mesoscopic family of material, the displayed generality and comprehensiveness indicate that the model may "understand" chemical bonds in a machine's way.

Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs.

Determining molecular structures is foundational in chemistry and biology. The notion of discerning molecular structures simply from the visual appearance of a material remained almost unthinkable until the advent of machine learning. This paper introduces a pioneering approach bridging the visual appearance of materials (both at the micro- and nanostructural levels) with traditional chemical structure analysis methods. Quaternary phosphonium salts are opted as the model compounds, given their significant roles in diverse chemical and medicinal fields and their ability to form homologs with only minute intermolecular variances. This research results in the successful creation of a neural network model capable of recognizing molecular structures from visual electron microscopy images of the material. The performance of the model is evaluated and related to the chemical nature of the studied chemicals. Additionally, unsupervised domain transfer is tested as a method to use the resulting model on optical microscopy images, as well as test models trained on optical images directly. The robustness of the method is further tested using a complex system of phosphonium salt mixtures. To the best of the authors' knowledge, this study offers the first evidence of the feasibility of discerning nearly indistinguishable molecular structures.

Engineering a PbrR-Based Biosensor for Cell-Free Detection of Lead at the Legal Limit.

Industrialization and failing infrastructure have led to a growing number of irreversible health conditions resulting from chronic lead exposure. While state-of-the-art analytical chemistry methods provide accurate and sensitive detection of lead, they are too slow, expensive, and centralized to be accessible to many. Cell-free biosensors based on allosteric transcription factors (aTFs) can address the need for accessible, on-demand lead detection at the point of use. However, known aTFs, such as PbrR, are unable to detect lead at concentrations regulated by the Environmental Protection Agency (24-72 nM). Here, we develop a rapid cell-free platform for engineering aTF biosensors with improved sensitivity, selectivity, and dynamic range characteristics. We apply this platform to engineer PbrR mutants for a shift in limit of detection from 10 μM to 50 nM lead and demonstrate use of PbrR as a cell-free biosensor. We envision that our workflow could be applied to engineer any aTF.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

SOLUBILITY POLYTEREME OF THE SYSTEM AMMONIUM SULPHATE - MAGNESIUM CHLORATE DEFOLIANT - WATER

The mutual influence of components in an aqueous system consisting of ammonium sulfate and magnesium chlorate defoliant was studied in a wide concentration range at a temperature of -27.0 to 4.5°C. On the state diagram of the system, the fields of crystallization of ice, magnesium chlorate hexahydrate, ammonium sulfate, and the compound (NH4)2SO4 ∙ 2NH4CIO3 ∙ 2MgSO4 ∙ H2O are delimited, which was identified and characterized by chemical, X-ray phase, thermal, and IR spectroscopic methods of analysis.