Search Results (502)

Graphene quantum dots (GQDs) have emerged as a promising class of carbon-based nanomaterials owing to their unique optical properties, tunable surface chemistry, excellent biocompatibility, and high physicochemical stability. These features make GQDs particularly attractive for the development of advanced biosensing platforms. This review provides a comprehensive overview of recent progress in the design, synthesis, and functionalization of GQDs, with a primary focus on their applications in biomedical and biosensors. Various synthesis approaches, including top-down, bottom-up, and chemical methods, are critically discussed in relation to their impact on structural and optical properties. The role of surface engineering and heteroatom doping in modulating sensitivity, selectivity, and signal transduction mechanisms is also highlighted. Furthermore, recent advances in GQD-based biosensors for the detection of clinically relevant biomarkers, environmental analytes, and pathogens are systematically summarized, with emphasis on analytical performance metrics such as sensitivity, selectivity, and limit of detection. In addition, complementary biomedical applications, including bioimaging and therapeutic platforms, are briefly discussed to provide a broader context for the multifunctionality of GQDs. Finally, current challenges and future perspectives toward the rational design of high-performance GQD-based biosensors are outlined. Full article

Background: Coffee quality is strongly influenced by origin-related factors, or terroir, which shape chemical composition and sensory characteristics. In the specialty coffee sector, where authenticity, traceability, and flavour distinctiveness drive value, understanding the molecular basis of sensory attributes, particularly perceived intensity, is essential. Methods: This study combined analytical chemistry and explainable artificial intelligence to explore relationships between volatile composition, coffee origin, and sensory intensity. Roasted and ground single-origin coffees from five provenances were analysed using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS). A Support Vector Machine (SVM) classifier discriminated coffee origins based on volatile profile, and SHapley Additive exPlanations (SHAP) identified key compounds. Ridge Regression (RR) was applied to predict sensory intensity values assigned by an expert panel. Results: The SVM model classified coffee origins with 91% accuracy, and SHAP analysis highlighted the volatiles most responsible for differentiation. RR predicted sensory intensity with R² = 0.88 and RMSE = 0.38, linking molecular profiles with panel-assigned intensity scores. Conclusions: This approach connects molecular profile with packaging-declared aroma intensity, offering an indirect yet informative link to sensory perception and illustrating the potential of data-driven methods in sensory science. Overall, the proposed explainable AI approach provides a transparent and reproducible connection between chemical composition, sensory traits, and perceived quality. This strategy supports more objective and traceable quality assessment systems, aligning analytical precision with sensory expertise, which is an essential step toward the evolution of quality control in industrial applications. Full article

GPU-accelerated quantum chemistry programs can dramatically reduce the time required for electronic structure calculations, yet most existing implementations either retrofit GPU kernels onto legacy CPU codebases or optimize individual kernels without addressing workflow-level integration overhead. We present GANSU (GPU Accelerated Numerical Simulation Utility), an open-source quantum chemistry framework written entirely in CUDA/C++ that integrates GPU-accelerated kernels for electron repulsion integrals, Fock matrix construction, and post-Hartree–Fock (post-HF) methods into a unified, GPU-resident execution pipeline. The key design principle is to eliminate host–device data transfers between computational stages by keeping all intermediate data, including density matrices, integral buffers, and Fock matrix replicas, on the GPU throughout the self-consistent field (SCF) iteration, combined with runtime-selectable integral strategies (stored ERI, resolution-of-the-identity, and Direct-SCF) that adapt to system size and available memory. On an NVIDIA H200 GPU, GANSU achieves end-to-end speedups of up to $52\times$ over PySCF for SCF, $45\times$ for MP2 on molecules with up to 470 basis functions, and $44\times$ for FCI, while outperforming GPU4PySCF by up to $34\times$ for FCI, across a range of molecular systems with up to 650 basis functions. The framework further provides analytical energy gradients and geometry optimization with nine algorithms, all operating within the same GPU-resident data flow. These results demonstrate that workflow-aware kernel integration, not just kernel-level optimization, is essential for realizing the full potential of GPU acceleration in scientific computing. GANSU is publicly available under the BSD-3-Clause license. Full article

The globalization of the food supply chain presents complex challenges for safety assurance within a highly fragmented regulatory landscape. This review synthesizes the frameworks of eight influential jurisdictions—including the European Union (EU), the United States, China, and Codex Alimentarius—to evaluate how legal mandates function as regulatory drivers that guide the evolution of analytical chemistry. By examining legislation on Maximum Residue Limits (MRLs), positive list systems, and method validation guidelines (e.g., SANTE), we demonstrate that strict preventive controls have established chromatography coupled with tandem mass spectrometry (LC/GC-MS/MS) as the universal standard for multi-residue screening. We show that global regulatory fragmentation is not merely an administrative artifact, but is rooted in divergent toxicological philosophies and localized dietary exposure models. This regulatory heterogeneity requires analytical laboratories to adopt a posture of “defensive technological redundancy,” forcing them to continuously optimize targeted methods against the strictest global default limits (e.g., 0.01 mg/kg). We establish that this continuous methodological escalation for ultra-trace quantification has reached practical and operational limits. Consequently, we conclude that the future of food safety testing must transition from static target-list compliance toward adaptable, non-targeted chemical profiling using High-Resolution Mass Spectrometry (HRMS), enabling laboratories to proactively address emerging contaminants, food fraud, and the complexities of modern food matrices. Full article

Biomass pyrolysis has emerged as a flexible platform for converting low-value residues into higher-value energy carriers (bio-oil, biochar and gas) and carbon-rich materials, with realistic potential for negative emissions when biochar is deployed in long-lived sinks. Over the last decade, three developments have driven the field forward: first, a finer mechanistic understanding of devolatilization and secondary reactions; second, major improvements in analytical techniques for characterising feedstocks and products; and third, more rigorous techno-economic and life-cycle assessments that place pyrolysis in a broader energy-system context. Recent experimental work on forestry and agro-industrial residues has clarified how biomass composition, ash chemistry and operating conditions jointly govern product yields, energy content and stability. Parallel advances in GC×GC–MS, high-resolution mass spectrometry, NMR and thermogravimetric methods have shifted the discussion from bulk “bio-oil” and “char” to families of molecules and well-defined structural domains, which can be deliberately targeted by reactor and catalyst design. Data-driven models, ranging from support vector machines applied to TGA curves to ANFIS and random forests for yield prediction, are now accurate enough to support process screening and multi-objective optimisation. At the system level, commercial fast pyrolysis biorefineries report overall useful energy efficiencies on the order of 80–86%, while slow pyrolysis configurations centred on biochar can be economically viable when carbon storage and co-products are appropriately valued. Thermodynamic analyses confirm that indirect gasification via fast-pyrolysis oil sacrifices some energy and exergy efficiency relative to direct solid-biomass gasification but may offer logistical and integration advantages. This review synthesises recent work on (i) feedstock and process characterisation; (ii) state-of-the-art analytical methods for bio-oil, biochar and gas; (iii) modelling and machine-learning tools; and (iv) energy-system deployment of pyrolysis products. Throughout, the emphasis is on how characterisation and modelling inform concrete design choices and on the trade-offs that arise when pyrolysis is considered as part of a wider decarbonisation portfolio. By integrating laboratory-scale characterisation with system-level modelling, this review aligns biomass pyrolysis with several United Nations Sustainable Development Goals (SDGs). The optimisation of thermochemical conversion pathways for forestry and agro-industrial residues directly supports SDG 7 (Affordable and Clean Energy) by enhancing the efficiency of bio-oil and syngas production. Furthermore, the deployment of biochar as a stable carbon sink for negative emissions and soil amendment addresses SDG 13 (Climate Action) and SDG 15 (Life on Land). By converting low-value waste streams into high-value energy carriers and chemicals within a circular bioeconomy framework, the research further contributes to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). Full article

Agriculture in the Pacific is driven primarily by small-scale private farmers, many of whom do not have access to soil testing services or advice, nor the means to interpret analytical results into soil management and agronomic recommendations. Soil degradation through the process of acidification poses a significant risk to food and income security as it directly threatens crop productivity. The nutritional quality of food crops may also be affected through sub-optimal nutrient uptake by plants and nutrient imbalances. The dataset reported here provides a useful platform for the development of a decision-support tool (DST) that will assist Fiji farmers in understanding and managing soil pH and soil acidity. The DST will enable making informed decisions about liming to help correct soil pH. To support this development, historical soil pH data available from the Pacific Soils Portal were combined with updated analyses of agricultural soils from 17 locations in Viti Levu Island (Fiji) collected during a field campaign undertaken in August 2025. The soils were sampled at two depth intervals (0–15 and 15–30 cm) and analyzed for pH using a variety of methods. These methods included direct field measurements using a portable pH-meter as well as traditional laboratory determinations. Of the soils sampled, it was found that most soils exhibited pH levels below 7, which were observed for both depth intervals. Across all samples taken in 2025, it was found that 54.3% of them had soil pH < 5, 38.6% had soil pH between 5 and 6, and 7.1% had pH > 6 (based on soil pH_1:5 soil-to-water method). Depending upon specific land uses, climate and cropping intensity, it was recommended that routine liming be built into soil fertility management programs to help farmers overcome soil acidity-related constraints to production. Liming frequency, timing of application and application rate will need to be determined for specific soil and cropping situations; however, it was suggested that soil pH was not changed by more than 1 unit each time lime was applied. Such an approach should reduce the risk of soil organic matter loss through accelerated mineralization, which would be challenging to restore in that environment if soils remained under continuous cropping. The analytical information contained in this article expanded and updated the datasets available in the Pacific Soils Portal. Furthermore, this work provided an opportunity to build analytical expertise in aspects of soil chemistry at local organizations to support academic and extension activities as well as the ongoing development of the Pacific Soils Portal. Full article

Protein glycation is a nonenzymatic modification that links sugar chemistry to molecular aging and chronic disease. Sequential reactions involving Schiff bases, Amadori products, and reactive α dicarbonyl intermediates generate advanced glycation end products (AGEs) that irreversibly alter protein structure and function. AGEs also act as ligands for the receptor for advanced glycation end products (RAGE), initiating oxidative stress, inflammation, and tissue remodeling. This review synthesizes the molecular pathways of AGE formation, their structural diversity, and the biological factors influencing glycation kinetics. Advances in analytical detection methods—including fluorescence spectroscopy, LC–MS/MS, and immunochemical approaches—are highlighted for their role in monitoring AGE accumulation. Particular attention is given to the contribution of glycation to diabetes, cardiovascular disease, neurodegeneration, and cancer, alongside emerging therapeutic strategies to limit AGE formation or block AGE–RAGE signaling. Glycation thus represents a central mechanism in human disease pathogenesis and an emerging therapeutic frontier. Full article

Background: The quantification of Dexamethasone Sodium Phosphate (DSP) in injectable formulations is significantly hindered by the spectral overlap of the stabilizer creatinine within the UV region. This study aims to develop a green first-order derivative (D¹) spectrophotometric method to resolve this analytical challenge. Methods: Distilled water was utilized as a sustainable solvent, aligning with green chemistry principles. To ensure high specificity, a matrix-matching calibration strategy with a constant 1:2 (w/w) DSP:creatinine mass ratio across the entire concentration range was employed. DSP was determined using the zero-crossing technique, measuring the D¹ amplitude at λ_ZC ≅ 231.3 nm, where the creatinine contribution is nullified. Results: Linearity was established for DSP concentrations between 4.0–16.0 μg/mL (R² > 0.99). Method validation, as per ICH Q2 (R1) guidelines (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use), demonstrated excellent accuracy (mean recovery of 99.85%) and precision (RSD < 2%). Conclusions: The proposed method offers a rapid, cost-effective, and eco-friendly alternative for the routine quality control of DSP injectables, eliminating the necessity for complex chromatographic separation techniques. Full article

In this study, we consider a potential expressed as a hyperbolic-sine function aiming to achieve the energy eigenvalues in a closed form, that is, as an analytical expression. Based on this, the Schrödinger equation is constructed within the framework of non-relativistic quantum mechanics and is tackled by using the Asymptotic Iteration Method. The potential in question was previously addressed in the literature. As an alternative, we obtain the complete energy spectrum in a closed form for the single-well regime of the potential function, by way of the quasi-exact solvability where the system has analytical energy eigenvalues once a certain condition is met, or a relation between the potential parameters is satisfied. This is provided by the applicability of the Asymptotic Iteration Method to both quasi-exact and numerical solutions. Thus, the effects of the potential parameters on the energy spectrum can be seen separately. We conclude that the accuracy of the obtained closed-form energy spectrum is quite high as evidenced by the strong agreement with the numerically obtained ones. Furthermore, it is seen that this consistency improves as the energy level increases. The obtained analytical expression can also be used as a simple analytical model for vibrational spectrum of molecular systems described by anharmonic single-well potentials. Full article

Micro- and nanoplastics (MNPs) are being found, with growing frequency, in agroecosystems, where soils function as major sinks and direct interfaces with food crops. This review shows an integrated soil–plant–food analytical framework and synthesizes evidence on MNPs behavior in soils (dispersion, aging, aggregation), plant uptake pathways (root vs. foliar, including atmospheric deposition), tissue translocation, and plant physiological responses. Across crop species and exposure conditions, convergent patterns included oxidative stress, disruption of nutrient homeostasis, impaired photosynthesis, and growth penalties, with magnitude modulated by particle size, polymer type, and surface chemistry within specific soil–plant contexts. Occurrence of MNPs in edible tissues of leafy, root, and fruit vegetables is critically appraised, as well as its implications for food safety and potential dietary exposure. Key uncertainties persist, including heterogeneous analytical methods, scarce long-term field datasets, and limited alignment between laboratory doses and environmental concentrations. These constraints translate into priorities for exposure assessment and risk governance, including the need for standardized metrics, harmonized quality criteria, and field-scale monitoring aligned with agronomic practices. By re-centering the analysis on crops and food systems while acknowledging human exposure implications, the review provides a decision-oriented basis for research and mitigation. Full article

Electrochemiluminescence immunoassay (ECLIA) is widely used in clinical diagnostics owing to its high sensitivity, broad dynamic range, and excellent analytical stability. However, the influence of magnetic bead deposition behavior on electrochemiluminescence (ECL) signal performance remains insufficiently characterized. In this study, a quantitative evaluation method for magnetic bead distribution uniformity on the electrode surface was established and applied to optimize fluidic parameters in an ECLIA measurement system. By combining microscopic imaging with image analysis, magnetic bead spreading behavior under different flow conditions was systematically characterized and correlated with luminescence signal intensity. Optimization of the flow rate (18.46 µL·s⁻¹) improved bead distribution uniformity and resulted in a 26.32% increase in luminescence intensity without altering bead coverage or assay chemistry. The optimized system was further validated using thyroid-stimulating hormone (TSH) detection, showing a linear response over 0.016–120 µIU·mL⁻¹ (R² > 0.996) and high consistency with a commercial analyzer (R² = 0.998) from Roche. These results demonstrate that quantitative control of magnetic bead distribution provides an effective strategy for improving ECLIA performance and offers a general optimization framework for bead-based electrochemiluminescence systems. Full article

Using disposable screen-printed electrodes faces major challenges when attempting to monitor a continuous process, especially in systems where there is pronounced adsorption, fouling, degradation, or in cases of irreversible electrochemical reactions. Methylene Blue (MB) exhibits some therapeutic properties and is commonly used as a redox reporter in DNA sensors, but is also considered a toxic pollutant in aquatic systems. MB demonstrates strong adsorption to carbon materials, which prevents its electroanalytical determination in multiple measurements with a single electrode. Our work details direct electrochemical determination of MB with only the native carbon screen-printed working electrode as sensing material and optimization of the analytical method. In batch mode, we significantly improved sensitivity and interelectrode reproducibility by introducing a prepolarization step, but successive measurements in lower concentrations were not feasible due to strong adsorption. A fully customizable, modular flow cell was 3D printed to allow in operando replacement of the planar screen-printed three-electrode system after measurement during continuous flow. As confirmed by mechanical properties testing, the rigid polyacrylate upper section of the flow cell provides structural stability, combined with a flexible TPU lower section which enables effortless sensor hot swapping and effective sealing during flow. With an optimized hot swapping flow detection method, MB was detected via square wave voltammetry with a sensitivity of 65.59 µA/µM and a calculated LOD of 7.75 nM, which outperforms similar systems from the literature. We envisage this approach can be integrated into low-cost continuous environmental monitoring systems or in-line quality control, especially in flow chemistry synthesis. Full article

Antimicrobial resistance (AMR) threatens global health, particularly through the rise of multidrug-resistant tuberculosis (MDR-TB) and other critical bacterial infections such as methicillin-resistant Staphylococcus aureus (MRSA). Rifamycins remain frontline antibiotics but are increasingly undermined by resistance. Here, we introduce a click-enabled platform for the synthesis of C8-functionalized rifamycins, which can be converted in a single additional step into efficacious 3′-hydroxy-5′-aminobenzoxazinorifamycins (bxRifs) and enzymatically into 25-deacetylated rifamycins (deAcRifs), providing access to novel antibacterial scaffolds that expand beyond the scope of traditional C8 modifications. Accordingly, we establish a modular strategy for late-stage analog development of the complex natural product rifamycin S, wherein azido and alkyne functionalities are installed via tailored core chemistry and converted into 1,2,3-triazoles through copper(I)-catalyzed click chemistry. Another key feature of this work is the development of systematic HPLC purification methods, enabling the isolation of analytically pure compounds despite structural complexity. The resulting analogs exhibit distinct antibacterial profiles, notably against Gram-positive bacteria including MRSA and Streptococcus mutans, informing structure–activity relationships and offering a foundation for further optimization. This approach supports the rapid diversification of rifamycin scaffolds to combat the escalating threat of AMR, while also establishing a foundation for future discovery through bioorthogonal applications. Full article

Green Analytical Chemistry (GAC) provides a framework for reducing hazardous reagents, energy consumption, and waste. The topic has gained momentum across many chemical industries over the past 25 years; however, progress in implementing sustainable methods and conducting greenness assessments within forensic laboratories has been comparatively slow. The purpose of this review is to highlight green approaches to analytical separation methods, including greenness assessment metrics, that have been reported in the literature for forensic chemistry and toxicology applications and to raise awareness of GAC in the forensic field. Recent scientific literature highlights promising advances in greener sample preparation and chromatographic approaches, particularly in forensic toxicology and seized-drug analysis. Emerging trends include the use of green solvents, bio-based and deep eutectic solvent systems, and the rapid expansion of microextraction techniques such as SPME, LPME, MEPS, FPSE, and DLLME, which reduce solvent volumes, minimize waste, and support higher-throughput workflows. Parallel developments in portable and miniaturized chromatographic instrumentation such as miniaturized LC–MS systems with increased detection specificity and Lab-on-a-Chip applications show promise for in situ measurements in the field. Ambient ionization mass spectrometry—in particular, DESI and DART—has had a major impact on forensic chemistry by providing tools for the rapid and direct analysis of chemical compounds in complex matrices with little or no sample preparation. Greenness assessment tools—including AGREE, AGREEprep, Eco-Scale, GAPI, and BAGI—are increasingly applied to evaluate analytical methods in forensic chemistry and toxicology, including those used for novel psychoactive substances. Although many green methodologies are well documented, their routine implementation remains limited. The continued integration of green solvents, microextractions, portable instrumentation, and standardized greenness metrics will be essential for advancing sustainable forensic separations. Full article

White Analytical Chemistry (WAC) provides a holistic framework for evaluating analytical methods by balancing analytical performance, environmental sustainability, and practical efficiency. Existing WAC assessment tools offer structured evaluation but often lack flexibility or comprehensiveness. To bridge this gap, we introduce the Whiteness Evaluation for Chemical Analysis (WECA) tool as a dynamic, web-based application that enables customizable, context-aware assessment of analytical methods. WECA allows users to select 2–4 criteria per RGB domain (Red: analytical performance; Green: environmental impact; Blue: practical efficiency), assign user-defined weights, and visualize results through an intuitive color-coded interface. The tool calculates a composite WECA score (%) that reflects overall method “whiteness”. Three case studies, covering HPLC-DAD, micellar electrokinetic chromatography, and electrochemical sensing, demonstrate WECA’s applicability and its ability to highlight method strengths and weaknesses across diverse analytical scenarios. WECA represents a step toward more adaptable, transparent, and visually intuitive method evaluation in alignment with the evolving principles of WAC. Full article