Analytical Model Research Articles

Numerical Modelling of Carrier Transport in Organic Field Effect Transistors

Background: Organic field effect transistors (OFETs), used in the fabrication of nanosensors, are one of the most promising devices in organic electronics because of their lightweight, flexibility, and low fabrication cost. However, the optimization of such OFETs is still in an early stage due to the minimal analytical and numerical models presented in the literature. Objective: This research presses to demonstrate a numerical carrier transport model based on the finite element method (FEM) to investigate the I-V characteristic of OFETs. Methods: Two various organic semiconductor materials have been included in the study, polyaniline and pentacene, where micro-scale, as well as nano-scale models have been presented. OFETs regarding channel length, dielectric thickness, and doping level impact have been studied. We nominated the threshold voltage, the on/off current ratio, the sub-threshold swing, and the field effect mobilities as the primary output evaluating parameters. Results: The numerical model has shown the criticality of the doping effect on tuning the device flowing drain current to exceed 300 μA saturation current, along with a threshold voltage of -0.1 V under a channel length of 30 nm. Conclusion: The study highlights the effectiveness of polyaniline over pentacene as nano-channel length OFET due to the boosted conductivity of polyaniline concerning pentacene.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

A Reaction-Based Approach to Colorimetric Detection of Organic Analytes in Water Using a Chlorine-Containing Carbocyanine Dye and Hypochlorite

Water quality control employs techniques mostly targeting individual analytes; group detection is also practiced, but the choice of group methods is limited, which supports interest in developing such methods. We have examined the interaction of hypochlorite with a chlorine-containing heptamethine carbocyanine dye in the presence of 30 organic and inorganic model analytes that were found to induce diverse color changes in the system. The main supposed mechanisms are retardation of the dye oxidation with hypochlorite (presumably by scavenging chlorine radicals) and substitution of chlorine atom in the dye by the most nucleophilic analytes (amines, amino acids, proteins, DNA, phenol). The grass-green substitution product is more contrastingly visible against the dark-purple hypochlorite oxidation product of the dye than against the original emerald-green dye. The indicator reaction is monitored photographically for 10–40 min and the images are processed using principal component analysis (PCA) or linear discriminant analysis (LDA), allowing for data convolution for the complex color transitions. Nitrogen compounds are discriminated from the others, and more reactive analytes (tryptophan, cysteine, bovine serum albumin, and DNA) are detected in the presence of less reactive ones in natural water. The system is promising for the development of group assays for dissolved organic matter and the discrimination of water samples.

Stable Halide Perovskite CsPbBr3 Nanocrystals Assisted by Covalent-Organic Frameworks for Electrochemiluminescence Analysis in an Aqueous Medium.

Lead halide perovskites have garnered attention as promising electrochemiluminescence (ECL) emitters owing to their superior photophysical characteristics. However, their poor water stability severely restricts their application in aqueous media for ECL. In this study, inorganic perovskite CsPbBr3 was assembled in situ in the imine-linked covalent-organic framework (COF-LZU1) as a novel ECL emitter. The expansive surface area and robust hydrophobic architecture of COF-LZU1 not only improved the water stability of CsPbBr3 but also guaranteed its exceptional ECL performance. The novel composite nanoluminescent material was coated onto an indium tin oxide (ITO) electrode via spin-coating and calcination processes to serve as an electrochemiluminescence (ECL) platform. A sensor was developed by combining a DNA hydrogel target-induced release system with a platform using ascorbic acid (AA) as a coreactant and T-2 toxin as the target analyte model. This method achieved a detection limit as low as 3.56 fg·mL-1 and was successfully applied to the analysis of the T-2 toxin content in corn samples. This study offers a novel path for the advancement of perovskite-based ECL emitters and their utilization in aqueous environments within the ECL field.

Effect of carbon price fluctuation on shore power system: a risk transmission analytics model

Effect of carbon price fluctuation on shore power system: a risk transmission analytics model

Ultrasensitive detection of histamine in spoiled meat employing silver nanoparticles decorated Perylene: An experimental-computational conjugation

Meat spoilage has been acquiring increasing attention recently and is directly associated with food safety and human health. Biogenic amines are the spying organic compounds fostered from the microorganism-mediated decarboxylation of amino acids during meat spoilage. Histamine, a biogenic amine acts as a model analyte and is toxic if consumed substantially. It is crucial to monitor histamine levels in meat due to its adverse effects. In this study, a simple and quick fluorescent sensor was fabricated for sensitive and selective detection of histamine. Citrate-capped silver nanoparticles (AgNP) were loaded onto Perylene (PER) to develop a sensing probe that was characterized using UV–visible, FTIR, XRD, and FESEM, and its optical behavior toward histamine was investigated. Moreover, the binding affinity between histamine and PER@AgNP was assessed using a DFT-based computer simulation. Under optimal conditions, the sensor showed linear relationships for histamine concentrations from 25 μM to 3200 μM with LOD 13.52 μM.

An Efficient Approach for Advancing Performance in Rapid Detection Based on Molybdenum Disulfide Nanoflower Supported Binary Transition Metal Oxides.

Binary transition metal oxides (BTMOs) have drawn considerable attention in recent years for their excellent catalytic properties and chemical stability in the sensing field. Regrettably, the loss of active site exposure originating from the agglomerate during preparation largely restricted their sensing applications. In this work, we report an efficient strategy for advancing the performance of BTMOs in rapid detection based on a 3D molybdenum disulfide nanoflower. The larger surface area, multiple active site exposures, and higher electrical conductivity promote the dispersion of BTMOs and the redox reaction of analytes on the surface of nanocomposites, thereby enhancing the sensitivity and widening the quantitative range. As a proof-of-method application, ferric vanadate (FeVO4) and ciprofloxacin (CIP) were chosen as model catalysts and analytes, respectively. This approach exhibits excellent sensitivity, selectivity, repeatability, and stability. The detection limit could be as low as 26.6 nM, and the linear range covered 3 orders of magnitude (from 0.1 to 500 μM). It also demonstrated good practicality in milk, honey, and drinking water with a recovery of 90.6% to 100.8%. To our knowledge, this is the first report on incorporating MoS2 into BTMOs for augmenting sensing performance in rapid detection.

Comparison of protein immobilization methods with covalent bonding on paper for paper-based enzyme-linked immunosorbent assay.

In this study, two methods were examined to optimize the immobilization of antibodies on paper when conducting a paper-based enzyme-linked immunosorbent assay (P-ELISA). Human IgG, as a test-capture protein, was immobilized on paper via the formation of Schiff bases. Aldehyde groups were introduced onto the surface of the paper via two methods: NaIO4 and 3-aminopropyltriethoxysilane (APTS) with glutaraldehyde (APTS-glutaraldehyde). In the assay, horseradish peroxidase-conjugated anti-human IgG (HRP-anti-IgG) binds to the immobilized human IgG, and the colorimetric reaction of 3,3',5,5'-tetramethylbenzyzine (TMB) produces a blue color in the presence of H2O2 and HRP-anti-IgG as a model analyte. The immobilization of human IgG, the enzymatic reaction conditions, and the reduction of the chemical bond between the paper surface and immobilized human IgG all were optimized in order to improve both the analytical performance and the stability. In addition, the thickness of the paper was examined to stabilize the analytical signal. Consequently, the APTS-glutaraldehyde method was superior to the NaIO4 method in terms of sensitivity and reproducibility. Conversely, the reduction of imine to amine with NaBH4 proved to exert only minimal influence on sensitivity and stability, although it tended to degrade reproducibility. We also found that thick paper was preferential when using P-ELISA because a rigid paper substrate prevents distortion of the paper surface that is often caused by repeated washing processes.

Performance of Predictive Models in Cervical Cancer Recurrence: A Systematic Review and Meta-Analysis of Biomarkers and Prognosis

This study investigates the use of Predictive Analytics Frameworks (PAF) for identifying biomarkers of recurrent cervical cancer and predicting prognosis. It addresses the limited comprehensive evaluations of the effectiveness of predictive models in this area, despite the growing application of machine learning in healthcare. The purpose of this systematic review and meta-analysis is to assess the performance of predictive analytics models in terms of sensitivity, accuracy, specificity, and the area under the curve (AUC-ROC) for identifying cervical cancer biomarkers and predicting prognosis. To address this research problem, a systematic review and meta-analysis was conducted, covering studies published between 2014 and 2024. A total of 1,515 studies were initially identified from the PubMed and Scopus databases, with 50 research studies meeting the inclusion criteria. Repeated measures ANOVA and meta-analysis were applied using data collected over an 8-year period to evaluate recurrence trends and the predictive power of various models. The findings suggest that predictive analytics models show significant potential for improving diagnostic accuracy in identifying cervical cancer biomarkers. However, the review also highlights several limitations, including the small number of included studies, heterogeneity across studies, and potential bias in retrospective analyses. In conclusion, while predictive analytics frameworks demonstrate promise in improving cervical cancer prognosis and biomarker identification, further research is required to validate these findings and assess their broader clinical utility. The study underscores the importance of continued exploration of predictive models to enhance decision-making in oncology.

Paper-based isotachophoretic preconcentration technique for low-cost determination of glyphosate.

Electrophoretic microfluidic paper-based analytical devices (e-µPADs) are promising for low-cost and portable technologies, but quantitative detection remains challenging. In this study, we develop a paper-based isotachophoretic preconcentration and separation method for the herbicide glyphosate as a model analyte. The device, consisting of two electrode chambers filled with leading and terminating electrolytes and a nitrocellulose strip as the separation carrier, was illuminated by a flat light source and operated with a voltage supply of 400V. Detection was accomplished using a simple camera. Colorimetric detection was optimized through competitive complexation between glyphosate, copper ions, and pyrocatechol violet as a dye. The buffer system was optimized using simulations, (i) ensuring the pH was optimal for the demetallation of the blue pyrocatechol violet-copper complex [PV] to the yellow free dye and (ii) ensuring the electrophoretic migration of glyphosate into the slower [PV] for the colorimetric reaction. A new data evaluation method is presented, analyzing the RGB channel intensities. The linear range was between 0.8 and 25µM, with a LOD of approximately 0.8µM. The ITP separation preconcentrated glyphosate by a factor of 820 in numerical simulations. The method may be applied to control glyphosate formulations, especially in developing countries where herbicide sales and applications are poorly regulated.

Ever aging AI/ML Models for Predictive Analytics in SaaS Subscription Management

The rapid evolution of Software as a Service (SaaS) has transformed how businesses manage subscriptions, necessitating innovative approaches to optimize customer retention and revenue growth. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) models in predictive analytics for SaaS subscription management. By leveraging advanced algorithms, organizations can analyze vast datasets to identify patterns in customer behavior, predict churn, and forecast subscription renewals. The study emphasizes the importance of feature selection and model training, highlighting how tailored predictive models can enhance decision-making processes. Furthermore, it discusses the implications of predictive analytics in personalizing customer experiences and improving service offerings, ultimately leading to increased customer satisfaction and loyalty. Through case studies and data-driven insights, this research aims to provide a comprehensive understanding of how AI/ML technologies can empower SaaS companies to navigate the complexities of subscription management and achieve sustainable growth.

Empirical investigation of a data analytics model in medical settings

This article centers around the development and analysis of machine learning and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of machine learning and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.

Plasmonic-Enhanced Colorimetric Lateral Flow Immunoassays Using Bimetallic Silver-Coated Gold Nanostars.

The colorimetric lateral flow immunoassay (cLFIA) has gained widespread attention as a point-of-care testing (POCT) technique due to its low cost, short analysis time, portability, and capability of being performed by unskilled operators with minimal requirement of reagents. However, the low analytical sensitivity of conventional LFIA based on colloidal gold nanospheres limits their applications for sensitive detection of trace amounts of target analytes. In this study, we introduced a novel plasmonic-enhanced colorimetric LFIA (PE-cLFIA) platform featuring bimetallic silver-coated gold nanostars (BGNS) with exceptional optical properties, leading to ultrahigh visual color brightness. The BGNS-based PE-cLFIA was successfully applied to detect a model analyte, low-calcium response V (LcrV), a virulence protein factor found in Yersinia pestis, the causative agent of bubonic plague. The PE-cLFIA sensing using BGNS-3 composed of 45 nm silver thickness showed a high visual colorimetric sensitivity with a detection limit as low as 13.7 pg/mL, which was around 50 times more sensitive than that of a traditional gold nanoparticle-based LFIA. In addition, the antibody-conjugated BGNS-3 showed excellent stability over 6 months. To illustrate the potential for clinical applications, we demonstrated that our LFIA platform for detecting LcrV spiked in human serum without any sample preprocessing exhibited a detection limit of 22.8 pg/mL. These results open up new opportunities for developing hybrid nanoparticle systems for sensitive POCT PE-cLFIA screening for infectious disease detection.

An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system.

Intelligent Video Analytics for Abnormal Event Detection

Surveillance cameras have become a primary tool for monitoring human movement and preventing unwanted or unintended activities. In today's world, security management professionals increasingly rely on video surveillance to combat crime and mitigate incidents that could adversely affect society. The number of surveillance camera installations has dramatically increased in both the private and public sectors, serving to monitor public activities effectively. Video surveillance is considered one of the most efficient methods for ensuring security. Installing a surveillance camera allows the captured footage to be transmitted to security personnel; however, merely having video footage is insufficient for identifying abnormal activities. To effectively analyze this footage, it is essential to integrate an intelligent system.This paper aims to design and implement an Intelligent Video Analytics Model (IVAM), also known as the Human Object Detection (HOD) method, for analyzing and detecting video-based anomalies and abnormal human activities. IVAM can be deployed alongside surveillance cameras in various public locations such as Institutions, airports, hospitals, shopping malls, and railway stations, allowing for the automatic identification of unusual events. The IVAM was experimented with using MATLAB software, and the results were thoroughly verified. The performance of IVAM was assessed by comparing the obtained results with those from existing approaches, demonstrating that IVAM outperforms contemporary methods concerning accurate anomaly detection, lower error rates, and higher classification accuracy.

“Kill two birds with one stone” role of PTCA-COF: Enhanced electrochemiluminescence of Au nanoclusters via radiative transitions and electrochemical excitation for sensitive detection of cadmium ions

Au nanoclusters (AuNCs) have attracted significant attention in electrochemiluminescence (ECL) owing to their excellent luminescent properties; however, achieving high-efficient ECL remains a formidable challenge. Despite the extensively reported emphasis on enhancing radiative transitions, it is imperative to acknowledge that both radiation and excitation play crucial roles in ECL, thereby requiring a dual-enhanced strategy for improving ECL efficiency. Herein, we introduced PTCA-COF, a dual-functional covalent organic frameworks (COFs) with the effect of “kill two birds with one stone” to achieve this purpose. The PTCA-COF not only acted as a carrier to effectively suppress the rotational vibration of surface ligands and reduce self-quenching by minimizing non-radiative transitions, but also acted as an electrosensitiser that facilitated the generation of electrogenerated holes, thereby promoting efficient transfer of hot electrons and enhancing excitation of AuNCs due to favorable energy level alignment. As a proof of concept, by utilizing cadmium ions (Cd2+) as a model analyte, the ECL aptasensing platform fabricated based on the dual-enhancement AuNCs-COFs ECL system demonstrated a broad linear response spanning from 1 pM to 5 nM and achieved an impressive low detection limit of 0.66 pM (S/N=3) with exceptional reproducibility and selectivity. The findings of this study not only presented a novel approach for the rational dual-enhancement of ECL efficiency, but also contributed to the advancement of their potential applications in the field of environmental monitoring.

Machine learning and public policy: Early detection of physical violence against children

Physical violence against children is a widespread and grossly underreported phenomenon with substantial short and long-term negative consequences. In Latin America and the Caribbean, 43% of children under the age of 15 experience corporal punishment at home, yet reporting rates are alarmingly low. This paper aims to demonstrate how household data can be considered for a future predictive analytics model in Argentina. Based on the 2019–20 MICS survey we apply machine learning techniques to predict physical violence against children (understood as physical discipline) at the household level in Argentina. The scope and potential benefits of using predictive models in this context are assessed, as well as the main limitations and risks. The results suggest that, by analyzing the situation of the 30% of households with the highest risk scores, 43 out of 100 households in which children experience physical violence could be identified at an early stage.

The Fabrication of Gold Nanostructures as SERS Substrates for the Detection of Contaminants in Water.

Gold nanostructures (AuNSs) were used to fabricate surface-enhanced Raman spectroscopy (SERS) substrates. These AuNSs were produced using the solid-state dewetting method from thin films. The fragmentation process was studied at 300 °C, with durations of thermal treatment of 1, 3, 6, and 12 h. These SERS substrates were then employed to detect Rhodamine B (RhB) as the model analyte, simulating a contaminant in the water at a concentration of 5 ppm. The morphology of the AuNSs was examined using SEM, which revealed a spheroidal shape that began to coalesce at 12 h. The size of the AuNSs was estimated to range from 22 ± 7 to 24 ± 6 nm, depending on the annealing time. The localized surface plasmon resonance of the AuNSs was determined using absorption spectroscopy, showing a shift as the annealing time increased. The SERS signals of RhB adsorbed on the AuNS substrates were validated by performing a 10 × 10 point map scan over each sample surface (1, 3, 6, and 12 h), and a comparative analysis showed no significant differences in the positions of the bands; however, variations in intensity enhancement ranged from 5 to 123 times at 6 and 1 h, respectively.

SWIEET-a salt-free alternative to QuEChERS.

The efficient extraction of various analytes from a wide spectrum of matrices with organic solvents is still a great challenge in analytical chemistry. Especially polar and charged compounds are hard to extract in combination with neutral analytes of intermediate to low polarity. The QuEChERS method is often chosen and has been adapted not only to the analysis of food samples, but also to environmental matrices (soil, wastewater) or biota. In this study, we overcome major drawbacks of QuEChERS such as low recoveries of charged analytes and impairment of downstream analysis by high salt loads. The new extraction method, applicable to liquid and solid samples, is called SWIEET (sugar water isopropanol ethyl nitrile extraction technique). Phase separation of the otherwise miscible extraction solvents water and acetonitrile is achieved by sugaring-out instead of salting-out. Extraction efficiencies were greatly improved by adding isopropanol to the acetonitrile phase. The concentrations of the additives glucose and isopropanol, as well as temperature, were optimized by a design of experiment. Further improvement was achieved through electro- or double-extractions. For all sample types tested (surface water, wastewater treatment plant effluent, tomato, soil, and oats), recoveries and precision were higher with SWIEET than with the established QuEChERS method. From wastewater treatment plant effluent, 75% recovery on average were achieved with our SWIEET method compared to 37% with QuEChERS for a model analyte mixture with polarities of logDpH7 = - 5.7 - 3.5. Higher recoveries and lower standard deviations compared to QuEChERS were achieved especially for polar and charged analytes such as metformin. Handling proved to be easy, since there was no additional solid phase and no tedious weighing of salts.

Preparation and application of multiple particle binding-liposomes for electrochemiluminescent signal amplification in bioassays.

In this study, multiple particle binding-liposomes (MPB-Lips), encapsulating the luminophore tris(2',2-bipyridyl)ruthenium (II) complex ([Ru(bpy)3]2+), were developed as an electrochemiluminescence (ECL) signal amplifier and were applied to detect the model analyte streptavidin (SA) using the indirect competitive ECL method. The MPB-Lips were prepared by mixing various ratios of two different liposomes-one containing a phospholipid with a primary amine group and a biotinyl group (BIO/NH2-Lip) and one containing a phospholipid with an N-hydroxysuccinimide group (NHS-Lip) to allow binding between particles via amide bonds. Quartz crystal microbalance analysis using SA-modified gold-coated quartz crystals showed that the frequency shift values of MPB-Lips gradually decreased in the order BIO/NH2-Lip:NHS-Lip = 1:0 < 1:1 < 1:3 < 1:5. This indicated that MPB-Lips were successfully formed. The indirect competitive ECL method using SA-modified gold electrodes showed that the 1:5-Lip system had greater sensitivity than the 1:0-Lip system-the limit of detection and quantification values for the systems were 1.84 and 6.30μgmL-1 for 1:0-Lip, and 1.20 and 1.74μgmL-1 for 1:5-Lip. Finally, the recovery of SA spiked in fetal bovine serum samples using the 1:5-Lip system showed good accuracy and precision with a recovery rate of 83-106% and relative standard deviation of 4-14%. Our study demonstrated that the MPB-Lips system was an effective and useful ECL amplifier and the ECL method using MPB-Lips could be applied to detect an analyte in a real sample.