Analytical Applications Research Articles

Miniaturized spectrometers based on graded photonic crystal films

Miniaturized spectrometers have become increasingly important in modern analytical and diagnostic applications due to their compact size, portability, and versatility. Despite the surge in innovative designs for miniaturized spectrometers, significant challenges persist, particularly concerning manufacturing cost and efficiency when devices become smaller. Here we introduce an ultracompact spectrometer design that is both cost-effective and highly efficient. The core dispersion element of this new design is a graded photonic crystal film, which is engineered by applying gradient stress during its fabrication. The film shows bandstop transmission spectral profiles, akin to a notch filter, enhancing light throughput compared to conventional narrowband filters. The spectral analysis, with a resolution of 5 nm and operating within the wavelength range of 450-650 nm, is conducted by reconstructing the spectrum from a series of such notch transmission profiles along the graded photonic crystal film, utilizing a sophisticated algorithm. This approach not only reduces manufacturing costs but also significantly improves the sensitivity (with a light throughput efficiency of 71.05%) and overall performance of the limitations of current technology, opening up new avenues for applications in diverse fields.

Numerical intercomparison of PHITS and Geant4 Monte Carlo codes for fast neutron inelastic scattering applications

Fast neutron inelastic scattering is a promising non-destructive assay technique for various analytical applications. As an active neutron interrogation technique, its performance is a function of various different factors and parameters that require optimization. Monte Carlo simulation codes are indispensable for such tasks. However, the internal simulation routines implemented in such codes can rely on different physical models that can yield discrepancies in the simulation results. In this work we conduct an intercomparison of PHITS and Geant4 codes performance in application to fast neutron inelastic scattering simulations. The goal of this paper is twofold. First, we explain the differences in code configuration with respect to gamma and neutron transport, as well as internal simulation routines. Second, we conduct a performance assessment of the two codes using two different measurement configurations. One configuration consisted of a source of gamma rays in a broad energy range (100–9000 keV) and a CeBr3 detector. The other configuration consisted of a monoenergetic 2.5 MeV fast neutron source, Fe, Nd, Dy, B targets and a CeBr3 detector. Selected simulation configurations were chosen with a goal to compare the performance differences in neutron energy distribution, produced prompt gamma rays and energy deposition in CeBr3 detector between the two codes. Results of our study reveal a good coherence of both codes performance in the application of fast neutron inelastic scattering simulations. The simulation geometries and observed differences are described in detail.

Temporal Assessment of Protein Stability in Dried Blood Spots.

The use of protein biomarkers in blood for clinical settings is limited by the cost and accessibility of traditional venipuncture sampling. The dried blood spot (DBS) technique offers a less invasive and more accessible alternative. However, protein stability in DBS has not been well evaluated. Herein, we deployed a quantitative LC-MS/MS system to construct proteomic atlases of whole blood, DBSs, plasma, and blood cells. Approximately 4% of detected proteins' abundance was significantly altered during blood drying into blood spots, with overwhelming disturbances in cytoplasmic fraction. We also reported a novel finding suggesting a decrease in the level of membrane/cytoskeletal proteins (SLC4A1, RHAG, DSC1, DSP, and JUP) and an increase in the level of proteins (ATG3, SEC14L4, and NRBP1) related to intracellular trafficking. Furthermore, we identified 19 temporally dynamic proteins in DBS samples stored at room temperature for up to 6 months. There were three declined cytoskeleton-related proteins (RDX, SH3BGRL3, and MYH9) and four elevated proteins (XPO7, RAN, SLC2A1, and SLC29A1) involved in cytoplasmic transport as representatives. The instability was governed predominantly by hydrophilic proteins and enhanced significantly with an increasing storage time. Our analyses provide comprehensive knowledge of both short- and long-term storage stability of DBS proteins, forming the foundation for the widespread use of DBS in clinical proteomics and other analytical applications.

Big data in visual field testing for glaucoma.

Recent technological advancements and the advent of ever-growing databases in health care have fueled the emergence of "big data" analytics. Big data has the potential to revolutionize health care, particularly ophthalmology, given the data-intensive nature of the medical specialty. As one of the leading causes of irreversible blindness worldwide, glaucoma is an ocular disease that receives significant interest for developing innovations in eye care. Among the most vital sources of data in glaucoma is visual field (VF) testing, which stands as a cornerstone for diagnosing and managing the disease. The expanding accessibility of large VF databases has led to a surge in studies investigating various applications of big data analytics in glaucoma. In this study, we review the use of big data for evaluating the reliability of VF tests, gaining insights into real-world clinical practices and outcomes, understanding new disease associations and risk factors, characterizing the patterns of VF loss, defining the structure-function relationship of glaucoma, enhancing early diagnosis or earlier detection of progression, informing clinical decisions, and improving clinical trials. Equally important, we discuss current challenges in big data analytics and future directions for improvement.

Electrocatalytic amplification of coreactant electrochemiluminescence using redox mediators

Electrochemiluminescence (ECL) has emerged as a sensitive analytical technique with a wide range of applications. While recent studies have begun to explore the role of redox mediators to facilitate ECL systems, this work extends these foundational insights by developing a comprehensive theoretical framework that supports, generalizes, and rationalizes mechanistic pathways borrowed from molecular electrocatalysis. This advancement is demonstrated through the electrocatalytic amplification of coreactant ECL within the [Ru(bpy)3]2+/TPrA system, utilizing a water-soluble redox-active Ir(III) complex as an electrocatalyst. Our investigation unveils previously uncharted mechanisms that enable the enhancement of Ru complex ECL at low electrode potentials, crucially without requiring the direct oxidation of [Ru(bpy)3]2+ or TPrA at the electrode, thus offering a deeper understanding of ECL activation through moelcular electrocatalysis. Through an integrated approach combining electrochemical analysis, spectroscopic investigation, and finite element modeling, we elucidate how the redox mediator critically modulates ECL efficiency by controlling the kinetics of radical species production and decay. The mediation by the Ir(III) complex not only governs the generation of TPrA radicals and excited [Ru(bpy)3]2+ states, thereby enhancing ECL intensity, but also the conditions under which ECL can be quenched. Further exploration of the mediator's redox characteristics provides predictive insights into ECL behavior, underscoring the mediator's redox potential as pivotal in determining ECL onset, peak potential, and intensity. This refined understanding paves the way for tailoring ECL systems for enhanced performance in analytical, imaging, and biomedical applications through the judicious selection of redox mediators.

Assessment of Analytical Techniques for Precise Quantification of Four Antiviral Drugs in Pharmaceutical Research and Development: A Comprehensive Review

: Precise measurement of drug concentration in pharmaceutical research is critical, especially for anti-viral drugs like boceprevir, elvitegravir, indinavir, and saquinavir that combat viral infections. It is well-known that analytical techniques play an imperative role in identifying and characterizing active pharmaceutical ingredients in biological samples and drug formulations. Moreover, precise drug assessment directly influences safety, stability, and efficacy while providing in-depth insight into drug pharmacokinetics. Other than this, analytical techniques also aid in identifying impurities, deteriorated products, and potential pollutants. Thus, reliable analytical methods have become crucial for addressing challenges imposed by complex drug formulations. The most commonly used analytical technique is UV spectrophotometry, which does not have the high sensitivity to detect complex drug formulations. In contrast, Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) merges two analytical techniques, chromatography and mass spectrometry, to accurately quantify biological samples. Furthermore, Ultra-Performance Liquid Chromatography (UPLC) provides enhanced resolution, faster analysis in short duration, and low solvent consumption in contrast to HPLC. This comprehensive review aims to critically assess each analytical approach's accuracy, applicability, selectivity, and limitation to provide valuable insights for researchers and analysts. Understanding the weaknesses and strengths of these analytical techniques will enable the researchers to select the suitable analytical method based on their needs and requirements for quality assessment, precise drug quantification, and optimal therapeutic efficiency. Eventually, this review intends to advance pharmaceutical research and development, specifically for anti-viral drugs, by ensuring the effective and secure administration of therapies.

A two-way analytical investigation of ancient gold coins: Elemental and colorimetric description of precious materials

The investigation of ancient artifacts is often constrained by their scarce availability and high protection and custody protocols. Among these, coinage represents an especially valuable kind-of-samples given their uniqueness and the subjacent information that is hidden behind their composition. Their analysis are often carried out using non-destructive techniques in order to avoid any alteration of the samples. In the field of Cultural Heritage analysis, smartphone-based methodologies have experienced a significant increase during the last few years, given their wide availability and ability to yield fast results. However, their analytical application demands a thorough and careful tuning during the methodology optimization. In this work, 21 historical gold and golden coins spanning a historical period of more than 2000 years have been analytically investigated. To that end, a two-fold approach has been implemented: first, the elemental composition has been analysed using portable X-ray fluorescence; and second, an innovative smartphone-based imaging method has been applied to measure their colour. Results allowed to describe the coins from their elemental profile, identifying some potentially debased ones, as well as some others not containing any gold. When possible, the results have been compared to previously reported cases, but our samples include some previously unreported cases representing new insights. All in all, this article provides new analytical data on unanalysed unique historical samples, in terms of their elemental profile and colorimetric properties, making use of an innovative, non-invasive nor destructive, fast and affordable colorimetric smartphone-based method to characterise historical coins.

Formulas of High MW Unknown Compounds from Accurate Mass Differences and Ranking of Best Candidates from First Principles.

The number of possible candidate formulas for high molecular weight unknown compounds (e.g., 7000-8000 Da for common 20-mer oligonucleotides) by high-resolution mass spectrometry is in the order of several hundred thousand even at the highest level of experimental accuracy. In demanding analytical applications involving new chemistries and synthetic routes where little is known about the chemical nature or mechanisms of formation of the unknown compounds (e.g., impurities), the generation of a short list of the most plausible formulas would be highly desirable. Such an approach has been developed in the current work. The concept of mass difference from a reference compound is introduced to simplify the approach and greatly reduce the number of possible formulas. The approach allows for the generation of candidate formulas by both the addition and subtraction of atoms to account for all possible molecular changes from the parent compound. A reduction of 3 orders of magnitude in the number of possible formulas has been achieved by the approach. Ranking of the formulas by the product of the sums of the absolute changes in the total number of all atoms and all heteroatoms in the proposed difference formula successfully ranked the correct formula within the top 10 from a list of 200-250 best candidate formulas. There is a tendency for the impurities to be formed involving the least change in the number of atoms and heteroatoms. ΔfHo and ΔfG'o values can be used as a complementary ranking system of the top candidates. The approach is applicable to unknowns in any other systems of high MW compounds.

Comparing novel small-angle x-ray scattering approaches for absolute size and number concentration measurements of spherical SiO2 particles to established methods

Biomedical analytical applications, as well as the industrial production of high-quality nano- and sub-micrometre particles, require accurate methods to quantify the absolute number concentration of particles. In this context, small-angle x-ray scattering (SAXS) is a powerful tool to determine the particle size and concentration traceable to the Système international d’unités (SI). Therefore, absolute measurements of the scattering cross-section must be performed, which require precise knowledge of all experimental parameters, such as the electron density of solvent and particles, whereas the latter is often unknown. Within the present study, novel SAXS-based approaches to determine the size distribution, density and number concentrations of sub-micron spherical silica particles with narrow size distributions and mean diameters between 160 nm and 430 nm are presented. For the first-time traceable density and number concentration measurements of silica particles are presented and current challenges in SAXS measurements such as beam-smearing, poorly known electron densities and moderately polydisperse samples are addressed. In addition, and for comparison purpose, atomic force microscopy has been used for traceable measurements of the size distribution and single particle inductively coupled plasma mass spectrometry with the dynamic mass flow approach for the accurate quantification of the number concentrations of silica particles. The possibilities and limitations of the current approaches are critically discussed in this study.

Unveiling the Power of Big Data: Insights into Big Data Analytics, Applications, and Advancements

Abstract: In the era of digital transformation, the exponential growth of data from multifaceted origins has fundamentally revolutionized decision-making and strategic planning across global industries. Big Data, distinguished by its immense volume, rapid velocity, diverse variety, and critical veracity, introduces unprecedented opportunities alongside formidable challenges. This journal explores the foundational concepts, methodologies, and applications of Big Data and analytics. It delves into the types and characteristics of Big Data, examines sources and collection methods, outlines analytical steps and tools, and addresses the inherent challenges. Through comprehensive analysis, this journal aims to provide insights into leveraging Big Data for informed decision-making, innovation, and competitive advantage in various sectors.

Harnessing big data analytics to revolutionize ESG reporting in clean energy initiatives

The integration of Big Data Analytics into Environmental, Social, and Governance (ESG) reporting holds significant potential to revolutionize clean energy initiatives. This paper examines the current challenges in ESG reporting, such as data collection and integration, data quality, regulatory compliance, transparency, and standardization. Big Data Analytics offers advanced techniques, including machine learning, artificial intelligence, predictive analytics, and real-time data processing, which can address these challenges. By leveraging diverse data sources—both structured and unstructured—clean energy projects can achieve more accurate environmental impact assessments, social impact evaluations, and governance analyses. The applications of Big Data Analytics are exemplified through various case studies, such as renewable energy projects, smart grids, and carbon offset programs. The benefits of these applications are vast, including enhanced data accuracy, improved decision-making, increased transparency, better stakeholder engagement, and cost efficiency. However, implementing Big Data Analytics in ESG reporting also presents challenges like data privacy, technological requirements, skills shortage, ethical considerations, and system integration. The future of Big Data Analytics in ESG reporting looks promising with emerging trends like blockchain for data transparency, IoT and sensor technologies, advanced machine learning models, and collaborative platforms. This paper concludes with a call to action for stakeholders to embrace these technologies, highlighting the transformative potential of Big Data Analytics in creating more effective and accountable clean energy initiatives.

Bibliometric analysis: Adoption of big data analytics in financial auditing

Bibliometric analysis is a widely used technique for investigating and studying scientific information. There is no previous research that explains bibliometric analysis related to the adoption of big data analytics in auditing. Thus, this research will fill the gap in previous research to examine bibliometric analysis related to the adoption of big data analytics in auditing. This paper employs bibliometric analysis on Scopus-indexed journals to examine the topic of big data analytics in audits, utilizing the VOSviewer tool. The objective of utilizing bibliometric analysis in this research is to ascertain the progression of articles concerning the application of big data analytics in the field of auditing. This article discusses the development of the number of publications and citations, the trend of publication researchers, the country of publication articles, the relationship between researchers, and the relationship between words with the topic of big data analytics in the period 2010–2022. This research reveals areas of application of big data analysis adoption in auditing. Qualitative research, especially library research, is the best method widely used among writers. This study provides several useful insights into the meaning of big data and data analysis, the benefits of using big data analysis in the audit process, and how the audit process can be made easier with big data analysis. Among the most interesting insights, the results suggest that big data implies vast amounts of data that exceed the limits of what can be stored and processed. Thus, the use of data analytics helps auditors reduce cognitive errors arising from large and diverse data sets. This bibliometric research presents the number of articles and citations of research publications, which authors and countries have the most research on this topic, and the keywords/terminologies that appear most frequently as well as the meaning of these keywords/terminologies.

Photoelectrochemical conversion for ion-selective electrodes based on CdS semiconductor film

BackgroundThis study presents a novel photoelectrochemical (PEC) conversion method for ion-selective electrodes (ISEs) based on CdS semiconductor film. The motivation stems from the need to enhance the sensitivity and precision of ISEs for various analytical applications. ResultsWe synthesized CdS film on FTO conductive glass via a hydrothermal method and utilized this electrode as the working electrode. Under visible light irradiation, CdS generated photocurrent that is proportional to its applied voltage within a large potential window of ∼0.80 V. Ascorbic acid (AA) effectively inhibited electron-hole complexation, enhancing photocurrent stability. Potential modulation from ISEs acting as the reference electrode further regulated photocurrent generation, demonstrating excellent sensitivity and linearity for a wide range of ion concentrations. The method was validated by detecting serum calcium levels, showing agreement with traditional ISEs potentiometry and ICP-OES methods. SignificanceThis photoelectrochemical conversion strategy offers a promising approach for sensitive and accurate ion detection, with potential applications in clinical diagnostics and environmental monitoring.

Demystifying oral history with natural language processing and data analytics: a case study of the Densho digital collection

PurposeThis study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives.Design/methodology/approachNLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA).FindingsThe researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators.Originality/valueThis case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.

A dual-oxidant sensing fluorescence probe for detecting hydroxyl radicals and hypochlorous acid in living cells and zebrafish

Reactive oxygen species (ROS) controls several physiological processes, including phagocytosis and pathogen destruction. Thus, it is necessary to developed reliable and effective tools to detect them. Here, in this work, we synthesized the first fluorescent probe (HR-SN) for dual-oxidant detection with quick, sensitive, and specific identification of •OH and HOCl in an aqueous solution. This probe contains a thiomorpholine group for detecting HOCl and an acetyl group that serves as the recognition site for the detection of hydroxyl radicals. HR-SN exhibits a distinct increase in fluorescence when exposed to •OH and HOCl, with a significant Stokes shift, rapid responses, and high sensitivity. The biological imaging results demonstrate that the probe is suitable for fluorescence imaging of living cells and living zebrafish. We anticipate that the concept and design outlined in this study could be a valuable reference for constructing fluorescence probes to detect reactive ROS for analytical and medical applications.

Enhancing the Performance and Accuracy in Real-Time Football and Player Detection Using Upgraded YOLOv5 Architecture

The study presents a significantly improved version of the YOLOv5 real-time object detection model for football player recognition. The proposed technique includes feature-tuning and hyper-parameter optimization methods that have been carefully selected to enhance both speed and accuracy, resulting in a superior real-time performance of the YOLOv5 architecture. Furthermore, the YOLOv5 model incorporates a SimSPPF module that enables multi-scale feature extraction with less computational power, making it a highly efficient and effective solution. We selected the GhostNet module to reduce complexity and the Slim scale detection layer for precise bounding box prediction. Our tests, conducted with recordings of multiple football matches, demonstrate that our model accurately detects both the football and players even in complex scenarios with occlusions and dynamic illumination. The suggested method outperforms the original YOLOv5n model in terms of precision, recall, and mean average precision at 0.5 IoU. It is also more computationally efficient. This method has potential applications in live broadcasting, player monitoring, and sports analytics. The upgraded YOLOv5 model demonstrates superior accuracy and efficiency compared to previous methods that rely on traditional image processing techniques or two-stage detectors. This makes it highly suitable for practical, real-world deployments.

Validated Area Under Curve Quantitative UV Spectrophotometric Analysis of Rebamipide in Drug Formulations

A very simple, accurately perfect, precisely repeatable procedure has been proposed for the estimate of rebamipide in medicines. Evaluation of previous works indicates the fact that, so far no UV spectrophotometric scheme for quantifiable estimate by area under curve technique for the drug rebamipide is not described. Therefore, there was a need to plan a different methodology to analyze the medicine employing dimethyl formamide in the form of solvent. This medication monitors Beer’s law in the range of concentration 20 to 200 ìg/mL in the ultraviolet range, particularly areas between 320 and 340 nm for are under curve because the absorption maximum was found to be 330 nm in the selected solvent. The recovery readings proved the offered technique’s accuracy and outcomes were authenticated par accordance with the International Council for Harmonisation (ICH) references. The discoveries were reasoned to be consistent as well as agreeable. As a consequence, this optional technique was successfully used to estimate rebamipide quantitatively in conventional analytical applications.

Application of Learning Analytics in Higher Education: Datasets, Methods and Tools

Application of Learning Analytics in Higher Education: Datasets, Methods and Tools

A review of deep eutectic solvents (DESs), Preparation, Classification, Physicochemical properties, Advantages and disadvantages

Since deep eutectic solvents (DESs) are readily available, inexpensive, highly biodegradable, and easy to synthesize, they are gaining popularity as a green alternative to hazardous organic solvents and traditional ionic liquids (ILs). DES is a mixture of hydrogen bond donor (HBD) and acceptor (HBA) that is viscous and non-water soluble. There are two main penalty areas of DES fabrication. The first is to reduce its operation temperature to lower that of these components. The second is to produce a molten salt with a melting point lower than its components. Therefore, DESs are being utilized more and more in a range of analytical chemical applications. Different extraction techniques have been used for this kind of salt. In this review, production methods, and classification of DESs were discussed. The most physical properties of DESs were demonstrated, including melting point, viscosity, density, surface tension, phase changes in behavior, analyte solubility, instrumental compatibility, and toxicity. It also included the main advantages and disadvantages of DES production.

Recent Advances in Fluorescent Biosensor: A Comprehensive Review

Herein, short review paper investigated the rapidly progress in fluorescent biosensors field and their significant influences on analytical chemistry applications. An electrical signal can be produced from a biological reaction using an integrated receptor-transducer device called a biosensor. Because there are so many uses for biosensors in the medical field, including medication delivery, environmental monitoring, water and food quality monitoring, and illness detection, biosensor design and development have become a top priority for scientists and researchers in the last ten years. In the beginning, we started to explain the basic principles of fluorescence. Then, we moved to discuss about the current advancements, creative sensor designs, and the fusion of materials science and nanotechnology. The paper highlighted the sensitivity of fluorescent biosensors by addressing a wide range of applications, including biological research, environmental monitoring, and medical diagnostics. This review paper censoriously assessed the present difficulties, such as interference and constrained dynamic range, and provided explanations into ongoing research. Advanced material and nanotechnology integration is emerging as a force that improves biosensor performance and broadens their uses. This study also discussed the future possible breakthroughs in biosensor technology and its broad use in a variety of scientific and societal fields.