Hasil untuk "Analytical chemistry"

Gregory S. Ducker, Joshua D. Rabinowitz

S. Schlücker

K. Dettmer, P. Aronov, B. Hammock

E. Ru, E. Blackie, M. Meyer et al.

M. Thompson, Stephen L. R. Ellison, R. Wood

Abstract Method validation is one of the measures universally recognized as a necessary part of a comprehensive system of quality assurance in analytical chemistry. In the past, ISO, IUPAC, and AOAC International have cooperated to produce agreed protocols or guidelines on the "Design, conduct and interpretation of method performance studies" [1], on the "Proficiency testing of (chemical) analytical laboratories" [2], on "Internal quality control in analytical chemistry laboratories" [3], and on "The use of recovery information in analytical measurement" [4]. The Working Group that produced these protocols/guidelines has now been mandated by IUPAC to prepare guidelines on the single-laboratory validation of methods of analysis. These guidelines provide minimum recommendations on procedures that should be employed to ensure adequate validation of analytical methods. A draft of the guidelines has been discussed at an International Symposium on the Harmonization of Quality Assurance Systems in Chemical Laboratory, the proceedings from which have been published by the UK Royal Society of Chemistry.

G. Schatz, K. Kelly, E. Coronado et al.

L. Frank, J. Friedman

J. C. Mcdonald, D. Duffy, Janelle R. Anderson et al.

D. Thévenot, K. Toth, R. Durst et al.

Abstract Two Divisions of the International Union of Pure and Applied Chemistry (IUPAC), namely Physical Chemistry (Commission I.7 on Biophysical Chemistry formerly Steering Committee on Biophysical Chemistry) and Analytical Chemistry (Commission V.5 on Electroanalytical Chemistry) have prepared recommendations on the definition, classification and nomenclature related to electrochemical biosensors; these recommendations could, in the future, be extended to other types of biosensors. An electrochemical biosensor is a self-contained integrated device, which is capable of providing specific quantitative or semi-quantitative analytical information using a biological recognition element (biochemical receptor) which is retained in direct spatial contact with an electrochemical transduction element. Because of their ability to be repeatedly calibrated, we recommend that a biosensor should be clearly distinguished from a bioanalytical system, which requires additional processing steps, such as reagent addition. A device which is both disposable after one measurement, i.e., single use, and unable to monitor the analyte concentration continuously or after rapid and reproducible regeneration should be designated a single use biosensor. Biosensors may be classified according to the biological specificity-conferring mechanism or, alternatively, to the mode of physico-chemical signal transduction. The biological recognition element may be based on a chemical reaction catalysed by, or on an equilibrium reaction with macromolecules that have been isolated, engineered or present in their original biological environment. In the latter cases, equilibrium is generally reached and there is no further, if any, net consumption of analyte(s) by the immobilized biocomplexing agent incorporated into the sensor. Biosensors may be further classified according to the analytes or reactions that they monitor: direct monitoring of analyte concentration or of reactions producing or consuming such analytes; alternatively, an indirect monitoring of inhibitor or activator of the biological recognition element (biochemical receptor) may be achieved. A rapid proliferation of biosensors and their diversity has led to a lack of rigour in defining their performance criteria. Although each biosensor can only truly be evaluated for a particular application, it is still useful to examine how standard protocols for performance criteria may be defined in accordance with standard IUPAC protocols or definitions. These criteria are recommended for authors, referees and educators and include calibration characteristics (sensitivity, operational and linear concentration range, detection and quantitative determination limits), selectivity, steady-state and transient response times, sample throughput, reproducibility, stability and lifetime.

U. Pöschl

S. Pandey

M. Trojanowicz

Amine Ballari, Rachid Haloui, Khadija Khaddam Allah et al.

In August 2024, the world health organization declared Monkeypox virus (MPXV) a public health emergency of international interest due to a significant increase in reported cases and the number of deaths. The MPXV is a transmissible disease spreading from animals to people via scratches, bites, or as food, then people spread it to other people by close contact including sexual activities, massages, kissing, day-to-day household contact, and caring for people with MPXV. This work aims to find novel MPXV receptor inhibitors with an application of fragment-based drug design strategy. A screening of >269,000 fragments from various online data bases has been conducted to determine their affinity for binding MPXV. Employing the 2022 version of Schrödinger software, a total number of 1600 fragments with the highest docking scores have been submitted to a fragment linking to generate 100 new molecules. The MPXV binding affinity and ADMET features of the top10 docking score ligands were then examined in more comprehensive detail. Lastly, the appropriate ligands were chosen for a molecular dynamics study in order to evaluate the stability of the ligand-receptor complex in the best three molecules. The discovered ligands might be investigated further in the field of Monkeypox inhibitor drug development.

Claudia Desiderio, Alexandra Muntiu

The application of Capillary Zone Electrophoresis (CZE) in proteomic analysis has increased significantly since the technique was hyphenated with high resolution mass spectrometry (HR-MS). The coupling was successful in combining the analytical features of high separation efficiency and resolving power of CZE with the precision of molecular mass measurements and sequencing of HR-MS analyzers for characterization of proteins and peptides and of their proteoforms, even in pre-digested, intact and native form. In this regard, CZE has made a strong contribution in the field of analysis and characterization of monoclonal antibodies (mAb) biotherapeutics, due to its high resolution power and separation mechanism based on differential migration, demonstrating challenging features for discriminating variants with rapid and high-efficiency analyses and for assessment of manufacturing quality control and safety of the final product. This review aims to highlight the most recent applications of CZE-MS in separation and characterization of mAb biotherapeutics based on the most recent literature (2022-2025). The various analytical approaches used in proteins MS characterization will be illustrated, with particular emphasis on the top-down approach, widely applied in this field of analysis and offering comprehensive characterization of proteoforms and exploration of protein interactions under native conditions. CZE-MS demonstrated an effective analytical strategy for the detailed characterization of mAb proteoforms with high potential for application in the challenging task of monitoring mAb release in advanced drug delivery systems.

Ilyes Batatia, William J. Baldwin, Domantas Kuryla et al.

Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery.

Muhammad W. MAZHAR, Muhammad ISHTIAQ, Mehwish MAQBOOL et al.

Arsenic contamination significantly affects rice yield and production. Recent studies have highlighted the potential of nanoparticles to mitigate heavy metal stress in cereals, although concerns about their phytotoxicity in agricultural systems have emerged. Coating nanoparticles may enhance their biocompatibility and reduce toxicity. In this study, we synthesized nano zinc oxide (ZnONPs) and methionine-coated nano zinc oxide (Met-ZnONPs), characterizing their properties using UV-Vis spectroscopy and transmission electron microscopy (TEM). Met-ZnONPs exhibited a blue shift and quantum confinement when characterized through UV-Vis and TEM. We aimed to compare the effects of seed priming with ZnONPs and Met-ZnONPs, hypothesizing that methionine coatings would enhance efficacy. Rice seeds were primed for 24 hours with either ZnONPs or Met-ZnONPs before sowing under both arsenic stress and non-stress conditions. We monitored intrinsic arsenic levels in soil and irrigation water and assessed arsenic content in rice grains post-harvest. Priming with 25 ppm Met-ZnONPs increased plant height, fresh weight, and activities of key antioxidant enzymes (ascorbate peroxidase, glutathione reductase, monodehydroascorbate reductase, and dehydroascorbate reductase) by 13.73%, 19.36%, 19.57%, 25.19%, 17.17%, and 14.4% respectively, compared to increases of 10.24%, 3.82%, 3.63%, 11.26%, 16.35%, 9.94%, and 7.06% for 50 ppm ZnONPs. Furthermore, 50 ppm Met-ZnONPs resulted in a 47.89% reduction in grain arsenic and a 36% decrease in hydrogen peroxide levels, while ZnONPs alone showed reductions of 22.28% in grain arsenic and 32% in hydrogen peroxide. These findings suggest that coating nanoparticles can enhance crop production by improving their biocompatibility and mitigating phytotoxic effects.

Natalia Prudente de Mello, Michelle Tamara Berger, Kim A. Lagerborg et al.

Objective: Parkinson’s disease (PD) is recognized as a systemic condition, with clinical features potentially modifiable by dietary intervention. Diets high in saturated fats and refined sugars significantly increase PD risk and exacerbate motor and non-motor symptoms, yet precise metabolic mechanisms are unclear. Our objective here was to investigate the interplay between diet and PD-associated phenotypes from a metabolic perspective. Methods: We explored PARK7 KO mice under chronic glycative stress induced by prolonged high-fat high-sucrose (HFHS) diet. We investigated metabolic consequences by combining classical metabolic phenotyping (body composition, glucose tolerance, indirect calorimetry, functional assays of isolated mitochondria) with metabolomics profiling of biospecimens from mice and PD patients. Results: We found this obesogenic diet drives loss of fat and muscle mass in early-onset PD mice, with a selective vulnerability of glycolytic myofibers. We show that PD mice and early-onset familial PD patients are under pervasive glycative stress with pathological accumulation of advanced glycation end products (AGEs), including N-α-glycerinylarginine (α-GR) and N-α-glycerinyllysine (α-GK), two previously unknown glycerinyl-AGE markers. Conclusions: Our results offer the first proof for a direct link between diet, accumulation of AGEs and genetics of PD. We also expand the repertoire of clinically-relevant glycative stress biomarkers to potentially define at-risk patients before neurological or metabolic symptoms arise, and/or to monitor disease onset, progression, and effects of interventions.

Sang Cheol Kim, Marco Gigantino, John Holoubek et al.

Capture of anthropogenic CO2 is critical for mitigating climate change, and reducing the energy cost is essential for wide-scale deployment. Solubility of inorganic carbon in aqueous solutions depends on the pH, and electrochemical modulation of the pH has been investigated as a means of CO2 capture and release. However, reported methods incur unavoidable energy costs due to thermodynamic penalties. In this study, we introduce a pH-independent redox chemistry that greatly lowers the thermodynamic energy costs by changing the pH without directly changing the [H+]. We show that the redox reaction of TEMPO molecules modulates the pH for capture and release of CO2 in a flow cell with an energy cost as low as 2.6 kJ/mol of CO2 corresponding to 0.027 eV/molecule. A molecular model, supported by MD and DFT simulations, is proposed of how the pH is decreased by 7.6 while largely avoiding the entropic energy cost associated with increasing the [H+]. We believe that this work showcases the potential of pH-independent redox chemistries for practical and cost-effective CO2 capture.

Lizhong Fu, Honghui Shang, Jinlong Yang et al.

The recently proposed Clifford augmented density matrix renormalization group (CA-DMRG) method seamlessly integrates Clifford circuits with matrix product states, and takes advantage of the expression power from both. CA-DMRG has been shown to be able to achieve higher accuracy than standard DMRG on commonly used lattice models, with only moderate computational overhead compared to the latter. In this work, we propose an efficient scheme in CA-DMRG to deal with \textit{ab initio} quantum chemistry Hamiltonians, and apply it to study several molecular systems. Our numerical results show that CA-DMRG can reach higher accuracy than DMRG using the same bond dimension, pointing out a promising route to push the boundary of solving \textit{ab initio} quantum chemistry with strong static correlations.

Thang D. Pham, Aditya Tanikanti, Murat Keçeli

Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations with methods ranging from tight-binding and machine learning interatomic potentials to density functional theory or wave function theory-based methods. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models like GPT-4o. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables smaller LLM models to match or exceed GPT-4o's performance in specific scenarios.