Efficient Analysis Method Research Articles

Optimised determination of arsenic and lead in tea samples using microwave-assisted digestion and ICP-MS: method validation and health risk assessment

ABSTRACT Heavy metal contamination in tea poses significant risks to consumer health, necessitating the development of accurate and efficient analytical methods for routine monitoring. This study aimed to optimise and validate a robust process for the simultaneous determination of arsenic (As) and lead (Pb) in dried tea samples using inductively coupled plasma mass spectrometry (ICP-MS) following microwave-assisted digestion. These toxic elements were selected due to their known health risks and their frequent detection in tea. The method was systematically optimised for digestion parameters and validated following EURACHEM guidelines to ensure accuracy and precision. Key validation metrics included linearity (R2 ≥0.9999), limits of quantification (LOQ) of 0.25 µg/kg for As and 1.5 µg/kg for Pb, sufficient for regulatory compliance. The method demonstrated excellent mean recoveries (75%–89%) and relative standard deviations (RSDs) below 15% across all fortification levels. Precision and accuracy were further verified through proficiency testing, yielding acceptable z-scores for both analytes. Application of the validated method to tea samples collected from Egypt revealed arsenic concentrations ranging from 0.018 ± 0.004 to 0.280 ± 0.070 mg/kg and lead concentrations from 0.083 ± 0.021 to 1.630 ± 0.407 mg/kg. Although detection rates were elevated, the calculated target hazard quotient (THQ) values for As and Pb were below the threshold of 1, suggesting no significant non-carcinogenic health risks associated with tea consumption. This study provides a reliable, cost-effective analytical framework for monitoring heavy metal contamination in tea products, supporting consumer safety and public health initiatives.

Energy feature extraction and visualization of voltage sags using wavelet packet analysis for enhanced power quality monitoring

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring. The study decomposes voltage sag signals into different frequency bands to extract key features for disturbance detection. We compare six commonly used mother wavelets (db1, db4, db10, dmey, sym5, and coif5) to identify the most suitable wavelet for voltage sag detection. The energy distribution curve analysis is used to evaluate the energy characteristics of each wavelet’s decomposition, with a focus on identifying the most effective signal features for PQ monitoring. The paper presents a thorough error analysis and compares the energy values extracted by different wavelet functions to demonstrate the reliability and accuracy of the proposed method. The results show that wavelet packet analysis significantly improves the detection and classification of voltage sag disturbances, providing a robust and efficient tool for real-time PQ monitoring. This study contributes to the development of advanced PQ monitoring systems by offering a more precise and computationally efficient method for voltage sag analysis, ultimately helping to protect electrical systems from potential damage and reducing operational costs. Wavelet packet analysis is applied as a novel feature extraction method for voltage sag detection by offering improved time-frequency analysis over traditional methods. Energy features are extracted from wavelet packet coefficients for sag identification. This work utilizes wavelet packet analysis to extract energy-based features for voltage sag detection.

High-sensitivity and stability electrochemical sensors for chlorogenic acid detection based on optimally engineered nanomaterials.

Developing cost-effective and efficient analytical methods is essential for detecting chlorogenic acid (CGA), as excessive consumption of CGA, despite its significant antioxidant, anticancer, and anti-inflammatory properties, can cause serious health problems. The remarkable progress and adjustable features of nanomaterials have significantly improved the analytical capabilities of electrochemical sensors for CGA. This review examines the use of optimally engineered nanomaterials in CGA electrochemical sensors, emphasizing the design and modification strategies of various nanomaterials. It starts with an introduction to the basic principles of electrochemical sensors, detailing their components and the analytical methods employed. Subsequently, the review explores how structural and compositional adjustments in electrocatalysts from different nanomaterial categories enhance CGA detection performance. In conclusion, it discusses the challenges and opportunities linked to designing nanomaterials for modified electrodes in CGA sensors. This review seeks to enhance the understanding of the connection between nanomaterial structures and the performance of CGA electrochemical sensors, offering new perspectives for the future design of highly efficient CGA electrochemical sensors.

Laser induced oxidation Raman spectroscopy as an analysis tool for iridium-based oxygen evolution catalysts.

The study of degradation behavior of electrocatalysts in an industrial context calls for rapid and efficient analysis methods. Optical methods like Raman spectroscopy fulfil these requirements and are thus predestined for this purpose. However, the iridium utilized in proton exchange membrane electrolysis (PEMEL) is Raman inactive in its metallic state. This work demonstrates the high oxidation sensitivity of iridium and its utilization in analysis of catalyst materials. Laser induced oxidation Raman spectroscopy (LIORS) is established as a novel method for qualitative, chemical and structural analysis of iridium catalysts. Differences in particle sizes of iridium powders drastically change oxidation sensitivity. Oxidation of the iridium powders to IrO2 occurred at a laser power density of 0.47 ± 0.06 mW μm-2 for the 850 μm powder and at 0.12 ± 0.06 mW μm-2 and 0.019 ± 0.015 mW μm-2 for the 50 μm and 0.7-0.9 μm powders respectively. LIORS was utilized to assess possible deterioration of an iridium electrocatalyst due to operation under electrolysis. The operating electrocatalyst exhibited higher oxidation sensitivity, suggesting smaller iridium particle size due to catalyst dissolution. Peak shifts of the IrO2 signal were utilized to assess differences in transformation temperatures. The operated electrocatalyst transformed to IrO2 at lower temperature (8 cm-1 redshift) relative to the pristine catalyst (10 cm-1 redshift), demonstrating that pre-oxidation of the iridium to amorphous IrOx during electrolysis diminishes the energy barrier needed for IrO2 formation. Thus, LIORS can be utilized as a straightforward screening method for the analysis of iridium electrocatalysts in the industrial application of PEMEL.

NON-MODIFIED Pt ELECTRODES ENABLING ACCURATE VOLTAMMETRIC QUANTIFICATION OF AMMONIA IN WATER SAMPLES

Even though ammonia is a natural component of aquatic environments, elevated concentrations indicate pollution and environmental degradation. Consequently, efficient analytical methods for monitoring ammonia levels are crucial for preserving water quality. Voltammetry offers a sustainable approach for NH3 quantification, combining good analytical performance with low sample and reagent consumption, minimizing waste generation. However, despite these advantages, the voltammetric determination of ammonia remains relatively unexplored. Therefore, this study investigated the electrochemical behavior of NH3 in alkaline solutions using a non-modified Pt electrode and evaluated the performance of differential pulse voltammetry (DPV) and square wave voltammetry (SWV) for NH3 determination. DPV provided superior performance, with limits of detection and quantification of 0.79 μmol L–1 (0.011 ppmN) and 2.6 μmol L–1 (0.036 ppmN), respectively, which comply with the maximum allowable ammonia concentration in natural waters according to Brazilian and European regulations. The voltammetric method successfully quantified ammonia in tap and river water samples, providing results concordant with the established Berthelot spectrophotometric method, at a 95% confidence level. Additionally, it is simple and fully accessible to non-electrochemist since no sophisticated procedures for electrode modification are needed. Finally, the high analytical performance of the proposed method makes it valuable for pollution monitoring in aquatic environments.

Large Language Model for Automating the Analysis of Cryoprotectants.

The rapid expansion of scientific literature necessitates developing efficient data extraction and analysis methods. This study presents an innovative approach to automating the extraction of cryoprotectant information from scientific publications using a generative pretrained transformer (GPT) model integrated with a Telegram bot interface. Our system processes and analyzes scientific articles to identify and extract relevant data on cryoprotectants and bacteria, significantly reducing the time required for researchers to gather essential information. Our method optimizes the workflow for researchers in cryopreservation and related fields by utilizing modern artificial intelligence technologies, specifically large language models. The Telegram bot, designed to be user-friendly, provides a comfortable and easy platform for quick data access, enhancing scientific research efficiency. The study's methodology involves data preparation, algorithm development, and system validation using a substantial data set of scientific articles. Results demonstrate the model's capability to accurately recognize and extract critical information, although some limitations in term specificity were noted. Our findings suggest that further refinement and training of the model can enhance its accuracy and reliability for specialized scientific applications.

Optimized Deep Neuro-Fuzzy Networks for Enhanced Cyber Forensic Analysis in Iot-Driven Big Data Ecosystems

The rapid expansion of Internet of Things (IoT) ecosystems has significantly increased the complexity and scale of cyber forensic investigations, necessitating innovative and efficient analytical methods. This study proposes an Optimized Deep Neuro-Fuzzy Network (DNFN) framework designed to enhance forensic capabilities in IoT-driven big data environments. The framework synergizes the feature extraction strengths of deep learning with the interpretative advantages of fuzzy logic, while employing optimization algorithms to fine-tune its parameters for improved performance. The DNFN framework addresses key challenges such as data heterogeneity, dynamic threat landscapes, and resource constraints in IoT ecosystems. Extensive experimentation on benchmark datasets demonstrates the framework's ability to achieve high accuracy in detecting cyber anomalies, reduce false positive rates, and support comprehensive forensic analysis. The proposed approach is a significant step toward enabling scalable, interpretable, and robust cyber forensic solutions tailored for the evolving complexities of IoT-driven networks.

Determination of Three Metal Ions (Cu2+, Pb2+, Cd2+) by Ultraviolet-visible Spectroscopy

Abstract The objective of this article review is the determination of the concentration or amount of trace metal ions using Ultraviolet-visible spectrophotometry. The maximum absorption wavelength (λ-max) of the three metallic ions (Cu+2, Pb+2, and Cd+2) that I have highlighted are equivalent to (755 nm, 100-380 nm, and 323.9 nm) when they are alone, However, when these metal ions interact with a reagent, the λ-max of each metal ion differs. Ultraviolet-visible (UV-Vis) spectrophotometry is deemed an efficient method for both qualitative and quantitative analysis of pollutants in a water environment. This succinct statement outlines the focus of the article review, emphasizing the application of UV-Vis spectrophotometry for analyzing trace metal ions in water samples. Also, the analytical technique measures the amount of single-color lighting absorbed via a colorless substance in the close ultraviolet light region of a range (between 200 and 400 nm). The process required to ascertain the “identity, strength, quality and purity” of such chemicals is included in the pharmaceutical analysis. Using calibration curves and absorption band correlation with certain ions to find concentration metal ion or analyte in the sample. A bibliometric analysis classifies the top 10,000 cited UV-Vis papers (2016-2017) into four clusters: nanoparticles, photocatalysis, crystals, and biological interaction of Ag and Au nanoparticles.

Circulating Extracellular Vesicles as Promising Biomarkers for Precession Diagnostics: A Perspective on Lung Cancer.

Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy, owing to their ubiquitous presence in bodily fluids and their ability to carry disease-related cargo. Recognizing their significance in disease diagnosis and treatment, substantial efforts have been dedicated to developing efficient methods for EV isolation, detection, and analysis. EVs, heterogeneous membrane-encapsulated vesicles secreted by all cells, contain bioactive substances capable of modulating recipient cell biology upon internalization, including proteins, lipids, DNA, and various RNAs. Their prevalence across bodily fluids has positioned them as pivotal mediators in physiological and pathological processes, notably in cancer, where they hold potential as straightforward tumor biomarkers. This review offers a comprehensive examination of advanced nanotechnology-based techniques for detecting lung cancer through EV analysis. It begins by providing a brief overview of exosomes and their role in lung cancer progression. Furthermore, this review explores the evolving landscape of EV isolation and cargo analysis, highlighting the importance of characterizing specific biomolecular signatures within EVs for improved diagnostic accuracy in lung cancer patients. Innovative strategies for enhancing the sensitivity and specificity of EV isolation and detection, including the integration of microfluidic platforms and multiplexed biosensing technologies are summarized. The discussion then extends to key challenges associated with EV-based liquid biopsies, such as the standardization of isolation and detection protocols and the establishment of robust analytical platforms for clinical translation. This review highlights the transformative impact of EV-based liquid biopsy in lung cancer diagnosis, heralding a new era of personalized medicine and improved patient care.

Automated Systems with Fluorescence Detection for Metal Determination: A Review.

Industrialization has led to environmental pollution with various hazardous chemicals including pollution with metals. In this regard, the development of highly efficient analytical methods for their determination has received considerable attention to ensure public safety. Currently, scientists are paying more and more attention to the automation of analytical methods, since it permits fast, accurate, and sensitive analysis with minimal exposure of analysts to hazardous substances. This review discusses the automated methods with fluorescent detection developed for metal determination since 2000. It is evident that flow-injection analysis (FIA) with no preconcentration or with solid-phase preconcentration are predominant compared to liquid-phase preconcentration systems. FIA systems are also more widespread than sequential injection analysis (SIA) systems. Moreover, a significant number of works have been devoted to chromatography-based methods. Atomic fluorescence detectors significantly prevail over molecular fluorescence detectors. It must be highlighted that most of the methods result in good figures of merit and performance characteristics, demonstrating their superiority in comparison with manual systems.

Multi-step optimization with operational scenarios for hull form and propulsor design for pod-driven cruise ships.

With the increasing demand for reducing CO2 emissions by the International Maritime Organization (IMO), controlling a ship's energy consumption at the design stage is crucial for proposing a 'greener' design. Some efforts have been made to consider the Energy Efficiency Design Index (EEDI) and the Energy Efficiency Operational Index (EEOI); however, the latter remains highly complex and contentious. In this study, a multistep optimization analysis method was developed to integrate EEDI and EEOI evaluations during the design stage to meet low emission requirements. Additionally, an Efficient Operational Scenario Analysis Method (EOSAM) was established to consider operational energy efficiency. This method was applied to optimize the hull and propulsor design of a pod-driven cruise ship. By accounting for operating conditions in both calm water and wave scenarios, and utilizing high-fidelity computational fluid dynamics simulations and model test validation, the delivered power was reduced by up to 7.1% compared to the original design, and the EEOI decreased by an average of 16.7%.

Highly Water-Stable 2D MOF as Dual Sensor for the Ultra-Sensitive Aqueous Phase Detection of Nitrofuran Antibiotics and Organochlorine Pesticides.

Misuse of antibiotics and pesticides has led to hazardous effects on human health, livestock, agriculture, and aquaculture, which urges researchers to find simple, rapid, efficient, and cost-effective methods for quick on-site analysis of these organic pollutants with functional materials. Herein, a 2D chemically robust MOF: IITKGP-71, {[Cd(MBPz)(2,6-NDC)]·2H2O}n is strategically developed with ease in scalability and exploited as dual sensors toward the toxic antibiotic and pesticide detection via luminescence quenching in aqueous medium. The framework displays exceptional chemical robustness in water for 3 months, in an open atmosphere over 2months, and wide range of aqueous pH solution (pH = 3-12) for a day. IITKGP-71 can selectively quench the nitrofuran antibiotics (NFZ and NFT) and organochlorine pesticide DCN while remaining unaffected by other interfering antibiotics and pesticides, respectively. An excellent trade-off between high effectivity (high Ksv) and high sensitivity (low LOD)was achieved for the targeted analytes. The easy scalability, high chemical stability, fast responsivity, multi-responsive nature, recyclability with outstanding structural stability made this framework viable in playing a crucial role in safeguarding aquatic ecosystems and public health from the hazardous effects of antibiotics and pesticides.

A study of the dynamic transmission efficiency of the cycloid pin gear pair under dynamic characteristic analysis

Transmission efficiency is one of the main technical indicators for evaluating the transmission performance of a cycloid pin gear planetary reducer. The cycloid pin gear pair is a crucial component of such a reducer. When the design parameters of the cycloid pin gear pair are determined, its transmission efficiency is often considered a constant, which significantly hinders accurate calculations of the transmission efficiency of the cycloid pin gear planetary reducer. To address this issue, this paper introduces a dynamic transmission efficiency analysis method specifically tailored for the cycloid pin gear pair, grounded firmly in dynamic characteristic analysis. Firstly, a comprehensive and meticulous dynamic performance analysis method is proposed for the cycloid pin gear pair, enabling precise calculations of dynamic transmission efficiency. Secondly, an optimization framework is introduced, with a primary focus on enhancing the transmission efficiency of the cycloid pin gear pair. A transmission efficiency assessment model is established as the objective function, which takes into account the pressure angles of both the driving gear and the driven gear at the meshing position. To demonstrate the practical utility and effectiveness of these methodologies, an illustrative example is presented. The results obtained from this example underscore a remarkable improvement in the overall transmission performance of the system. This enhancement is characterized by substantial gains in transmission efficiency and effective vibration control, highlighting the significance and practicality of the proposed approaches.

Protocol for Therapeutic Drug Monitoring Within the Clinical Range Using Mid-infrared Spectroscopy.

Therapeutic drug monitoring (TDM), which involves measuring drug levels in patients' body fluids, is an important procedure in clinical practice. However, the analysis technique currently used, i.e. liquid chromatography-tandem mass spectrometry (LC-MS/MS), is laboratory-based, so does not offer the short response time that is often required by clinicians. We suggest that techniques based on Fourier transform infrared spectroscopy (FTIR) offer a promising alternative for TDM. FTIR is rapid, highly specific and can be miniaturized for near-patient applications. The challenge, however, is that FTIR for TDM is limited by the strong mid-IR absorption of endogenous serum constituents. Here, we address this issue and introduce a versatile approach for removing the background of serum lipids, proteins and small water-soluble substances. Using phenytoin, an antiepileptic drug, as an example, we show that our approach enables FTIR to precisely quantify drug molecules in human serum at clinically relevant levels (10 μg/mL), providing an efficient analysis method for TDM. Beyond mid-IR spectroscopy, our study is applicable to other drug sensing techniques that suffer from the large background of serum samples.

Hyperspheric Integral Reliability Method for efficient reliability analysis of geotechnical ultimate limit states

This paper introduces the Hyperspheric Integral Reliability Method (HINT) for efficient reliability analysis of geotechnical ultimate limit states. The method is motivated by the mechanism of the Shear Strength Reduction Method (SSRM), which is often employed to calculate the factor of safety for geotechnical ultimate limit states. HINT exploits the observation that a factor of safety, computed by the SSRM, is not only a pointwise estimate of safety, but also a measure of distance to the failure limit along a radial direction in the space of the shear strength parameters. This observation is utilized to transform the reliability integral in the hyperspheric coordinate system and develop an efficient estimator of failure probability for geotechnical ultimate limit states. HINT was examined on several benchmarking problems, demonstrating stable and highly efficient performance on low to medium dimensional reliability problems. HINT can be also applied to general reliability problems if a radial search mechanism is implemented as in the SSRM. Given that the SSRM is already available in most commercial geotechnical software packages, HINT is perfectly suited to advance geotechnical reliability analyses and make them accessible to a wider set of use-cases.

Finite Tube Method for buckling analysis of tubular members using Fourier-approximation for the displacements

In this paper an efficient numerical method for the static analysis of cylindrical tubes is introduced. The method is designed for the linear buckling analysis of wind turbine support towers which are, most typically, built up from conical and/or cylindrical cans. Accordingly, the developed method uses cylindrical tube segments as elementary building blocks, along with specialized shape functions, and is named the Finite Tube Method. Within a tube segment the displacements are approximated by two-dimensional Fourier series. The curved nature of the surface is directly considered in the kinematic equations. The segments are joined and/or supported to the ground by constraint equations or by elastic links. In the current implementation internal stresses are determined in a simplified way: the circumferential stress distributions are calculated from the internal forces/moment by classic strength of material formulae, while the longitudinal distribution within each segment is quadratic. The considered internal forces/moments are: normal force, shear force, bending moment, and torsional moment. The internal forces can be arbitrarily combined. In the paper the underlying derivations are briefly summarized, then the method is demonstrated and validated by numerical examples, comparing the results to analytical and alternative numerical solutions. The authors are actively developing the method and will provide future work on utilization of the method for buckling mode identification and decomposition, as well as practical advancements to make the method a useful tool in the engineering design and analysis of wind turbine support towers.

Analysis of the Contribution of Energy, Industry, Agriculture and Food Production to Improving the Quality of Life of Citizens in Turkic States with Efficiency and Super Efficiency Analysis Methods

Analysis of the Contribution of Energy, Industry, Agriculture and Food Production to Improving the Quality of Life of Citizens in Turkic States with Efficiency and Super Efficiency Analysis Methods

Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq) to Study DNA Methylation Using Low Amounts of DNA

Background/Objectives: DNA methylation is a key epigenetic mark involved in regulating gene expression. Aberrant DNA methylation contributes to various human diseases, including cancer, autoimmune disorders, atherosclerosis, and cardiovascular diseases. While whole-genome bisulfite sequencing and methylated DNA immunoprecipitation (MeDIP) are standard techniques for studying DNA methylation, they are typically limited to a few samples per run, making them expensive and low-throughput. Therefore, an automation-friendly method is needed to increase throughput and reduce costs without compromising data quality. Methods and Results: We developed a novel method called Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq), which can be used to analyze many DNA samples in parallel, requiring only small amounts of input DNA. In this method, 10 different DNA samples were fragmented, purified, barcoded, and pooled prior to immunoprecipitation. In a head-to-head comparison, we observed a 99% correlation between MeDIP-Seq performed individually or combined as Mx-MeDIP-Seq. Moreover, multiplexed MeDIP led to more than 95% normalized percent recovery and a 25-fold enrichment ratio by qPCR, like the enrichment of the conventional method. This technique was successfully performed with as little as 25 ng of DNA, equivalent to 3400 to 6200 cells. Up to 10 different samples were processed simultaneously in a single run. Overall, the Mx-MeDIP-Seq method is cost-effective with faster processing to analyze DNA methylome, making this technique more suitable for high-throughput DNA methylome analysis. Conclusions: Mx-MeDIP-Seq is a cost-effective and efficient method for high-throughput DNA methylation analysis, offering faster processing and reduced sample requirements. This technique makes DNA methylome analysis more accessible for large-scale studies.

Simultaneous metabolomics and lipidomics analysis based on 4in1 online analysis system reveal metabolic signatures in atherosclerotic mice

Developing efficient and comprehensive analysis methods for metabolomics and lipidomics in the biological tissues and body fluids is essential for understanding the disease mechanisms. Although various two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) methods have been proposed to expand metabolite coverage, achieving higher efficiency in integrated metabolomics and lipidomics studies remains a technical challenge. In this work, a novel 4in1 online analysis system with excellent reproducibility and mass accuracy was constructed for metabolomics and lipidomics study in various biological samples from atherosclerotic mice. This system enabled the simultaneous detection in both positive and negative ion modes with extensive polarity separation in a single analytical run. Using the 4in1 online analysis system, we identified distinct but complementary metabolic signatures associated with atherosclerosis in different biological samples. Specifically, a total of 230 and 170 differential metabolites or lipids were detected in mice plasma samples and aortic tissue samples, respectively, including glycerophospholipids, sphingolipids, fatty acyls, glycerolipids, carboxylic acids, and pyrimidine nucleosides. Additionally, atherosclerosis-related metabolic pathways involved in biosynthesis of unsaturated fatty acids, sphingolipid metabolism, cholesterol metabolism, glycerophospholipid metabolism, and choline metabolism further revealed. These findings demonstrate that the novel 4in1 online analysis system is a faithful, stable and powerful tool for comprehensive metabolomics and lipidomics studies in complex biological matrices.

Time-variant reliability analysis using phase-type distribution-based methods

The performance of engineering structures often varies over time due to the randomness and time variability of material properties, environmental conditions and load effects. This paper proposes phase-type (PH) distribution-based methods for efficient time-variant reliability analysis. The core of the proposed methods is to approximate the extreme value of a stochastic process as a PH distributed random variable, and treat the time parameter as a uniformly distributed variable. Consequently, the time-variant reliability problem is transformed into a time-invariant one. Three representative time-invariant reliability methods, first-order reliability method (FORM), importance sampling (IS) and adaptive Kriging (AK) surrogate model-based IS method (AK-IS), are integrated with the PH distribution-based approximation strategy to form the proposed methods, namely PH-FORM, PH-IS and PH-AKIS. The efficiency and accuracy of these methods are demonstrated through three examples. All codes in the study are implemented in MATLAB and provided as supplementary materials.