Abstract:

Paris polyphylla (Chonglou), a medicinal herb documented in Shennong’s Classic of Materia Medica and a key component of formulas such as Yunnan Baiyao, is a rare and endangered plant prized for its bioactive steroidal saponins, notably polyphyllin I (PPI) and II (PPII). However, its pharmacological potential is hampered by inefficient extraction and unreliable compound identification. Herein, we developed a sustainable and efficient extraction strategy using ultrasound-assisted deep eutectic solvents (DES), optimized via an L₉(3⁴) orthogonal experimental design. Extraction efficiencies across the seven Paris species ranged from 2.04% to 16.51%, achieved by systematically optimizing key parameters such as the choline chloride-to-ethanol molar ratio (1:1.8), material-to-liquid ratio (1:20 g mL^-1), and extraction time (100 min). By ultra high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis, PPI and PPII were quantified using specific retention times and characteristic fragment ions, revealing content ranges of 3.282–21.452 mg g^-1 and 4.201–17.975 mg g^-1, respectively. This methodology provides a robust platform for quality control and standardization of Paris-derived medicines, while paving the way for sustainable utilization and in-depth study of its steroidal saponins.

Abstract: Purpose : Data stability is a critical factor in ToF-SIMS single-cell analysis. However, various factors, such as sample processing, instrument condition, and data acquisition, can introduce uncertainties into ToF-SIMS data. Correcting this data is vital, yet current methods mainly focus on total ion current normalization or using consistent substrates. No specific correction method exists for ToF-SIMS single-cell metabolomics. Methods: This study utilizes the Norm-SVR, commonly used methods for correcting large-scale metabolomics data, for the correction of ToF-SIMS single-cell metabolomic analysis and assesses its performance in comparison to traditional total ion current normalization. Results and Conclusion: The results suggest that Norm-SVR effectively diminishes batch effects and reduces variability, thereby underscoring the method's efficacy and practicality. This approach is expected to improve data quality assurance in extensive ToF-SIMS analytical datasets.

Abstract:

In this study, a novel material obtained from shredded maize stalk (MS) was functionalized using Alizarine Red S (ArS), a complexing agent that contains -OH and -C=O groups in its structure (MS-ArS). The obtained material MS-ArS was employed in adsorption experiments for Mn²⁺, Pb²⁺, Cu²⁺, Cr³⁺, Zn²⁺ and Fe³⁺ (Mⁿ⁺) removal. Initially, complex formation between (Mⁿ⁺) and ArS in buffer solution at pH 4 and 10 was investigated using UV-Vis spectrometric method. The functionalization process of MS was done at pH = 2, 4, 6, 8, and 10. The results showed that the best functionalization was obtained at pH=2. After functionalization study, Mⁿ⁺ adsorption onto MS-ArS at pH 4 and 10 was tested. Mⁿ⁺ adsorption proved to be pH dependent. It was observed that pH=10 was the optimum medium for Mⁿ⁺ adsorption. MS-ArS has affinity for Mⁿ⁺ in the following order Fe³⁺>Cu²⁺>Zn²⁺>Mn²⁺>Pb²⁺>Cr³⁺. The results demonstrate also remarkable desorption rates (D(%)) when 0.5 M HCl is used as regeneration solvent: 94% for Cu²⁺, 92.4% for Fe³⁺, 91.7% for Cr³⁺, 90.8% for Zn²⁺, 90.3% for Pb²⁺, and 86.1% for Mn²⁺. These findings highlight the potential of this sustainable material for effective adsorption and recovery of the complexing material in order to respect the principle of circular economy approach.

Abstract: Distribution water-extractable particulate (colloid) organic matter (WEOM) in narrow (nano- to micrometer) size fractions of chernozem soil by sequential filtration on track-etched membranes were studied. Multimodal (IR and fluorescence) two-dimensional correlation (2D-COS) spectroscopy was used. Protocols for attenuated total reflectance (ATR) FTIR of WEOM are proposed. ATR-FTIR 2D-COS provides larger volume of information on characteristic bands compared to traditional FTIR, especially in C–H ranges (3000–2800 and 1450–1300 cm–1). Fluorescence excitation-emission-matrix 2D-COS showed that the indexes and ratios of humic- to protein-like compounds are reproducible, exhibit significant variation among size fractions, with maximum amounts of saturated humic-like compounds in the largest (2–10 μm) and finest factions (0.01–0.03 μm) while medium fractions (0.05–1 μm) are dominated by fulvic acids and fresh organic matter. Heterospectral fluorescence–IR 2D-COS enhanced the accuracy of identification and assessment of WEOM group composition and showed that C–H IR band intensities correlate with tyrosine-like EEM bands and biogenic fluorescence in-dexes, while carboxylic components, with humate-like bands and humification fluo-rescence indexes. Element profiles in WEOM fractions correlate with fluorescence in-dexes; humification indexes, with P, S, Cr, Mg, Ca, Cu, and Zn; biogenic, with Mg, P, Cr, Cd, K, S, and Ca.

Abstract:

Additive manufacturing, particularly fused deposition modeling (FDM), has emerged as a promising approach for producing electrochemical sensors based on conductive thermoplastic composites. In this study, the effects of various printing parameters (extrusion temperature, layer height and width, printing speed, and the number of conductive layers) on the electrochemical performance of PLA/CB electrodes fabricated via FDM were investigated. Electrochemical impedance spectroscopy analyses showed that properly adjusting these parameters promoted the formation of more efficient conductive pathways and reduced charge transfer resistance during the monitoring of the redox behavior of the potassium ferrocyanide/ferricyanide probe. Furthermore, the applicability of the sensor was demonstrated through the determination of dopamine, achieving a detection limit of 0.16 µmol L⁻¹. Overall, the findings highlighted that optimizing printing conditions is essential for enhancing the electrochemical response of the sensors and further strengthened the potential of 3D printing as a promising route for the fabrication of electrodes for electroanalytical applications.

Abstract:

This study explores the pedagogical integration of Large Language Models (LLMs) into chemistry education through a practical chemometrics activity using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Graduate students employed Microsoft 365 Copilot (GPT-5) and Gemini to perform statistical analyses, dimensionality reduction, and classification tasks entirely via natural language prompts. The exercise covered exploratory data visualization, normality assessment, log transformation, Principal Component Analysis (PCA), and Partial Least Squares Discriminant Analysis (PLS-DA). Students compared raw and log-transformed datasets to investigate how preprocessing affected multivariate discrimination and predictive accuracy. Both LLMs generated reproducible results consistent with Jamovi software outputs and produced publication-quality plots including score, loading, VIP, and confusion matrix diagrams. Beyond technical proficiency, the activity enhanced students’ conceptual understanding of supervised and unsupervised learning while promoting critical evaluation of generative AI outputs. The findings demonstrate that LLMs can serve as accessible, interactive tools for teaching machine learning and chemometric analysis, lowering programming barriers and fostering data literacy.

Abstract: Coral reef ecosystems represent one of the most biodiverse and productive marine habitats, yet they are increasingly threatened by a range of anthropogenic stressors. Among these, pharmaceutical and personal care products (PPCPs) have recently emerged as contaminants of growing concern due to their persistence, bioaccumulation potential, and complex interactions within reef environments. This review synthesizes current research on the occurrence, transport pathways, and ecological impacts of PPCPs on coral reef systems. Evidence indicates that compounds such as UV filters, antibiotics, and endocrine-disrupting chemicals can impair coral physiology, disrupt symbiotic relationships with zooxanthellae, and contribute to bleaching events. The review further highlights the variability in coral species’ sensitivity to these contaminants, with documented effects ranging from DNA damage and oxidative stress to reduced growth and reproductive capacity. Despite advances in detection and risk assessment, significant knowledge gaps remain regarding long-term exposure, mixture effects, and the influence of local environmental conditions on contaminant toxicity. By consolidating recent findings, this review underscores the urgent need for targeted research and policy action to mitigate the threat of emerging contaminants to coral reef ecosystems.

Abstract: Integral membrane proteins (IMPs), which constitute 50–60% of drug targets, play es-sential roles in numerous biological processes but remain underrepresented in conven-tional bottom-up and in structural proteomics owing to their hydrophobicity and re-sistance to proteolysis. Although advances in IMPs proteomics have improved global IMPs detection, most efforts focus on proteome-scale protein identification rather than targeted structural analysis. Protein footprinting and cross-linking, two approaches in structural proteomics, require high sequence coverage and protein digestion to pep-tides of suitable length for structural elucidation, necessitating optimized digestion condition for individual IMPs. Here, we describe a digestion protocol tailored for structural mass spectrometry, applying it to an amphipathic IMP with distinct ex-tramembrane and transmembrane domains, as a model system. We evaluated the use of various protease–additive combinations and applied filter-aided sample prepara-tion (FASP) to remove detergents and surfactants efficiently prior to MS analysis. The optimized conditions consistently yielded >90% sequence coverage. Guided by MS re-tention time calibration and hydrophobic factor simulations, we identified a “sweet spot” for transmembrane peptide detection. Notably, although cleavable surfactants can enhance proteome-wide coverage, our results show that they are not essential for single protein studies as in structural proteomics. Instead, detergent removal, protease selection, and generation of suitably sized peptides are critical for enabling reliable bottom-up structural analysis of IMPs. The protocol developed here was successfully applied across several footprinting methods for structural studies of IMPs.

Abstract: Accurate analysis of trace elements in particulate matter (PM) emitted by brake systems critically depends on the filter selection and handling processes, which can significantly impact analytical results due to contamination and elemental interference from filter elemental composition. This study systematically evaluated two widely used filter types, EMFAB (borosilicate glass microfiber reinforced with PTFE) and Teflon (PTFE), for their suitability in trace element determination of brake wear PM10 collected using a tribometer setup. A total of twenty-three PM10 samples were analyzed, encompassing two different friction materials, to thoroughly assess the performance and analytical implications of each filter type. Filters were tested for their chemical background, handling practicality, and potential contamination risk through extensive elemental analysis by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). Additionally, morphological characterization of both filter types was conducted via scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) to elucidate structural features affecting particles capture and subsequent analytical performance. Significant differences emerged between the two filters regarding elemental interferences: EMFAB filters exhibited substantial background contribution, particularly for alkali and alkaline earth metals (Ca, Na, Mg, and K), complicating accurate quantification at trace levels. Conversely, Teflon filters demonstrated considerably lower background but required careful manipulation due to their structural fragility and the necessity to remove supporting rings, potentially introducing analytical variability. Statistical analysis confirmed that the filter material significantly affects elemental quantification, particularly when the collected PM10 mass is limited, highlighting the importance of careful filter selection and handling procedures. Recommendations for optimal analytical practices are provided to minimize contamination risks and enhance reliability in trace element analysis of PM10 emissions. These findings contribute to refining analytical methodologies essential for accurate environmental monitoring and regulatory assessments of vehicular non-exhaust emissions.

Abstract:

Background/Objectives: The stability of pharmaceutical compounds is a critical quality attribute; it is an essential step in the drug development process. Significant focus is required to understand the variation of quality pharmaceutical compounds under prevailing environmental storage conditions. Simultaneously, many issues arise in understanding updated regulations, knowledge of data sciences, and appreciation of common practices, presenting a challenge for defining a retest period and in predicting a prolongation of the shelf life of drug products. The purpose of this paper is to conduct a statistical study to assess stability and to forecast a prolongation of drugs shelf-life. Methods: A case study is suggested to identify the most appropriate statistical test for assessing stability. The results of physical and chemical tests are considered to detect changes and variability during different conditions (accelerate, intermediate and real). Results: In the stability study, minimal variability in the content of the substance of interest was obtained using the predictive interval approach over a period of 31 months, and an interval of ±1,2%. Conclusion: The example of the statistical study is given to provide different perspectives on statistical approaches for market approval.

Abstract: Lab on a Chip devices offer high efficiency, low volume and fast analytical measurement, but their use is still niche. A key component for these devices is the detector, and one common type of detection is fluorescence spectroscopy. However, in some cases the detector can be bulky and lose the appeal of small footprint devices. To make lab-on-a-chip devices truly compact, detectors must also be compact. In this paper we discuss the use of simple and low-cost commercial multispectral sensors for use in lab on a chip devices, more specifically for fluorescence detection. Which we demonstrate to allow detection on nanomolar scale with a very simple set up.

Abstract: Poly(amino acids) and gold nanoparticles are stable and biocompatible materials with distinguishing features which can be used to build nanocomposite electrochemical platforms for sensing applications. This communication presents the optimization of the building steps of these nanocomposite platforms using cyclic voltammetry. Screen-printed graphite electrodes were first modified by electropolymerizing various L-amino acids and then by electrodepositing gold nanoparticles. The electroactive sur-face area was calculated for all platforms, which were then applied in the electro-chemical oxidation of 1-naphthol as a model analyte: oxidation peaks were observed in all cases, with the current peak height increasing with increasing analyte concentra-tion, thus demonstrating the potential of nanocomposite platforms for developing electrochemical sensors and biosensors.

Abstract: Food irradiation is gaining popularity worldwide as a method for extending shelf life and controlling pests and diseases. Post-treatment irradiation doses are usually monitored using instrumental methods, which may be expensive, labor-intensive, time-consuming, and not allow for low-dose detection. We previously proposed a chemical fingerprinting strategy for estimating irradiation doses based on measuring the rate of an indicator reaction. However, the feasibility of dose assessment was demonstrated only for freshly irradiated samples. In this study, we investigated the feasibility of determining the order of magnitude of dose in irradiated raw potato tubers after several days of storage. The samples were extracted with water, and the extracts were introduced into oxidation-reduction and aggregation reactions carried out in a 96-well plate. The reaction rates were monitored by measuring absorbance and fluorescence of the reaction products, followed by chemo-metric processing. The feasibility to estimate doses to an order of magnitude (0, 100, 1000 Gy) was shown for storage time of 0, 2, and 6 days at 4°C. The accuracy of dose recognition on day 6 was at least 97% by using SoftMax regression (SR) or linear discriminant analysis (LDA). Irradiated and non-irradiated samples can be confidently distinguished using partial least square–discriminant analysis (PLS-DA). The reaction-based method of dose assessment is simple, rapid, and does not require sophisticated equipment.

Abstract: Understanding the dynamic evolution of electrocatalysts under operando conditions is critical for advancing sustainable energy conversion. However, interpreting complex multimodal time-series data remains challenging. In this work, we presents Multimode Operando GPT (MOGPT), a large language model-based framework for causal reasoning and performance prediction in electrocatalysis. MOGPT integrates multimodal data processing with a Temporal Causal Discovery Module, a Catalytic Evolution Knowledge Graph, and a Causal Consistency Loss to identify temporal and causal relationships in catalyst behavior. A large-scale dataset of causal question–answer pairs across various catalyst systems is constructed for benchmarking. Experimental results show that MOGPT achieves superior performance in spatio-temporal reasoning, causal inference, and performance prediction, while maintaining strong robustness and generalization. This approach highlights the potential of large language models for interpretable and data-driven discovery in electrocatalysis.

Abstract: In recent years, the interest in electrochemical biosensors has been constantly growing for epithelial cancer diagnosis and prognosis. The incorporation of the different nanomaterials as metal nanoparticles, magnetic nanoparticles, carbon nanomaterials, Metal-Organic Frameworks (MOFs), and nanocomposites, along with specific monoclonal antibodies, and nucleic acids (aptamers) has improved both sensitivity and specificity in these methodologies. In this review, we have presented examples of electrochemical biosensors for the determination of different epithelial cancer biomarkers. Based on many published literature examples, we have emphasized the recent application of one and multiplexed platforms for the quantification of epithelial cancer biomarkers. Finally, we discussed the possible way of development, challenges, and future perspectives in the area of electrochemical immuno-, apta-, and geno-sensors.

Abstract: Background/Objectives: Post-blast forensic investigations frequently involve oversized exhibits, such as vehicle fragments, concrete blocks, and metallic debris, which are of-ten contaminated with mixed explosive formulations. These heterogeneous substrates present significant challenges for residue recovery and analysis when conventional methods, optimized for small, homogeneous samples are applied. This study aimed to develop and evaluate forensic strategies tailored to oversized and fragmented evidence from mixed explosive detonations. Methods: Oversized exhibits from ANFO–nitrate detonations were examined using sequential swabbing, solvent extraction, and spatial subsampling techniques. Organic residues were characterized using Thin Layer Chromatography (TLC) and Gas Chromatography–Mass Spectrometry (GC–MS), while in-organic ions were identified through chemical spot tests and Fourier Transform Infra-red (FTIR) spectroscopy. The efficacy of syringe filtration was further assessed to improve the residue recovery efficiency. Results: GC–MS analysis of ether extracts con-firmed the presence of high-boiling petroleum hydrocarbons, consistent with diesel fractions in ANFO. Inorganic analyses revealed the presence of ammonium and potassium nitrate, whereas chlorate, perchlorate, and metallic additives were absent. Spatial subsampling enhanced the detection sensitivity, and syringe filtration produced the highest recovery yield by minimizing background interference. Conclusions: The integrated workflow, which combines targeted swabbing, sequential solvent extraction, syringe filtration, and complementary analytical methods—proved highly effective for investigating oversized post-blast exhibits. This approach improves residue recovery, enhances analytical reliability, and strengthens evidentiary interpretation, providing a robust framework for the forensic investigation of complex detonation events in the future.

Abstract:

Helichrysum species (Asteraceae) are renowned for their diverse phytochemical profiles and traditional medicinal applications. Among their specialized metabolites, phloroglucinol-α-pyrone derivatives represent a structurally unique and pharmacologically significant class of compounds. This review consolidates over five decades of phytochemical research, documenting 52 distinct compounds isolated from 11 Helichrysum species across the Mediterranean, African, and Iranian regions. The compounds are organized into structural subclasses, including monopyrones, dipyrones, and various phloroglucinol derivatives distinguished by their molecular scaffolds. Isolation yields reported in the literature range from trace amounts to relatively abundant constituents (0.48% w/w), with arzanol emerging as the most extensively studied compound. Bioactivity profiles reveal anti-inflammatory, antimicrobial, antioxidant, and antiparasitic properties, with arzanol demonstrating potent dual inhibition of mPGES-1 and 5-LOX. This comprehensive compilation provides essential reference data for future investigations into the chemistry and therapeutic potential of α-pyrone secondary metabolites from Helichrysum species.

Abstract: Baseline correction techniques are highly applicable in analytical chemistry. Consequently, there is a constant demand for universal and automated baseline correction methods. Our new procedure based on the Convolutional Autoencoder model (ConvAuto model) combined with an automated algorithm to Apply the Model (ApplyModel procedure) meets those expectations. The key advantage of this idea is its ability to handle 1D signals of various lengths and resolutions, which is a common limitation encountered for deep neural network models. The proposed procedure is fully automatic and does not require any parameter optimization. As our experiments have shown, the ApplyModel procedure can also be easily combined with other baseline correction methods that utilize deep neural networks, such as the ResUNet model, which extended it practical application too. The usability of our new approach was tested by its implementation for both simulated and experimental signals, ranging from 200 to 4000 point length. The results were compared with those provided by the ResUNet model.

Abstract:

The potential of the new algorithm for comparing experimental and reference values of gas chromatographic retention indices (RI) is discussed. This algorithm is designed to eliminate significant elements of uncertainty typical of numerous contemporary recommendations, primarily the fixed limiting values of permissible deviations, DRI = (RI_ref – RI_exp). The algorithm proposed implies the calculation of deviations DRI for selected most reliably identified constituents of multicomponent mixtures with known reference RI values, followed by calculation of coefficients of regression equations DRI = (RI_ref – RI_exp) = aRI_exp + b for total sets of analytes. These equations allow recalculating the experimentally determined RIs into the corrected values RI_corr = RI_exp + DRI. Such algorithm makes it possible to use reference RI values for semi-standard nonpolar polydimethylsiloxane phases (with 5% phenyl groups and others) for the comparison with data determined with standard nonpolar polydimethylsiloxanes and vice versa. It is applicable both to statistically processed reference data and to results of single measurements.

Abstract: Proteins are essential biological macromolecules that play key regulatory roles in all biological processes. Abnormalities in these processes are often reflected in proteins, manifesting as changes in their structure, sequence, folding state, stoichiometry, or spatial and temporal distribution. Proteins serve as biological targets for drugs and other therapeutics and can also function as therapeutic agents to restore normal biological functions by treating diseases. Hence, it is essential to study native protein species, their modifications, higher-order structures, and complexes, which can be extremely difficult due to the challenges in preserving their native conditions and the instrumental capability required for such analysis. High-resolution mass spectrometry (HRMS) instruments provide advanced technical capabilities to study intact protein species from their gas phase ions after the protein solution is sprayed into the mass spectrometers. However, there are debates about the gas-phase protein structures obtained through mass spectrometry and the resemblance to their biological native state. This review discusses various techniques for isolating, separating, and enriching intact protein species for their native mass spectrometry (nMS) analysis. Emerging technologies, such as automated sample preparation, ion mobility spectrometry, and ambient surface mass spectrometry, are briefly discussed. This review aims to serve as a general guideline for beginners, primarily focusing on the preanalytical strategies and critical instrument parameters for nMS analysis of intact proteins, proteoforms, protein complexes, and higher-order structures.