Reduced Analysis Time Research Articles

Characteristic pancreatic and splenic immune cell infiltration patterns in mouse acute pancreatitis

BackgroundA systemic evaluation of immune cell infiltration patterns in experimental acute pancreatitis (AP) is lacking. Using multi-dimensional flow cytometry, this study profiled infiltrating immune cell types in multiple AP mouse models.MethodsThree AP models were generated in C57BL/6 mice via cerulein (CAE) injection, alcohol and palmitoleic acid (EtOH + POA) injection, and alcohol diet feeding and cerulein (EtOH + CAE) injection. Primary pancreatic cells and splenocytes were prepared, and multi-dimensional flow cytometry was performed and analyzed by manual gating and computerized PhenoGraph, followed by visualization with t-distributed stochastic neighbor embedding (t-SNE).ResultsCAE treatment induced a time-dependent increase of major innate immune cells and a decrease of follicular B cells, and TCD4+ cells and the subtypes in the pancreas, whereas elicited a reversed pattern in the spleen. EtOH + POA treatment resulted in weaker effects than CAE treatment. EtOH feeding enhanced CAE-induced amylase secretion, but unexpectedly attenuated CAE-induced immune cell regulation. In comparison with manual gating analysis, computerized analysis demonstrated a remarkable time efficiency and reproducibility on the innate immune cells and B cells.ConclusionsThe reverse pattern of increased innate and decreased adaptive immune cells was consistent in the pancreas in CAE and EtOH + POA treatments. Alcohol feeding opposed the CAE effect on immune cell regulation. Together, the immune profiling approach utilized in this study provides a better understanding of overall immune responses in AP, which may facilitate the identification of intervention windows and new therapeutic strategies. Computerized analysis is superior to manual gating by dramatically reducing analysis time.

Fluorine loss determination in bioactive glasses by laser‐induced breakdown spectroscopy (LIBS)

AbstractFluoride‐containing bioactive glasses and glass‐ceramics in the SiO2‐P2O5‐CaO‐CaF2 system are of great interest for dental applications, where the precipitation of fluorapatite supports tooth remineralization. Fluorine quantification in those glasses is key to estimate thermal properties and crystallization tendency. This work presents a study on fluorine determination by laser induced breakdown spectroscopy (LIBS) in four melt‐derived glass powders with varying P2O5 concentrations. LIBS enables fluorine quantification with a reduced analysis time, minimal to no sample preparation, and high spatial resolution. The fluorine calibration curve was obtained from CaF2 and SiO2 mixtures as reference samples, and the fluorine loss upon glass melting has been determined as a function of P2O5 content. The P2O5‐free glass shows the lowest fluorine loss (13%), with HF volatilization likely being responsible for the loss. By contrast, the glass with the highest P2O5 content (11.33 wt%) exhibits the largest fluorine loss (55%), owing to additional mechanisms involving the volatilization of phosphorus species like POF3.

Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay

To provide a comprehensive analysis of small molecule genotoxic potential we have developed and validated an automated, high-content, high throughput, image-based in vitro Micronucleus (IVM) assay. This assay simultaneously assesses micronuclei and multiple additional cellular markers associated with genotoxicity. Acoustic dosing (≤ 2 mg) of compound is followed by a 24-h treatment and a 24-h recovery period. Confocal images are captured [Cell Voyager CV7000 (Yokogawa, Japan)] and analysed using Columbus software (PerkinElmer). As standard the assay detects micronuclei (MN), cytotoxicity and cell-cycle profiles from Hoechst phenotypes. Mode of action information is primarily determined by kinetochore labelling in MN (aneugencity) and γH2AX foci analysis (a marker of DNA damage). Applying computational approaches and implementing machine learning models alongside Bayesian classifiers allows the identification of, with 95% accuracy, the aneugenic, clastogenic and negative compounds within the data set (Matthews correlation coefficient: 0.9), reducing analysis time by 80% whilst concurrently minimising human bias. Combining high throughput screening, multiparametric image analysis and machine learning approaches has provided the opportunity to revolutionise early Genetic Toxicology assessment within AstraZeneca. By multiplexing assay endpoints and minimising data generation and analysis time this assay enables complex genotoxicity safety assessments to be made sooner aiding the development of safer drug candidates.

Online Microdialysis-High-Performance Liquid Chromatography-Inductively Coupled Plasma Mass Spectrometry (MD-HPLC-ICP-MS) as a Novel Tool for Sampling Hexavalent Chromium in Soil Solution.

Conventional soil solution sampling of species-sensitive inorganic contaminants, such as hexavalent chromium (CrVI), may induce interconversions due to disruption of system equilibrium. The temporal resolution that these sampling methods afford may also be insufficient to capture dynamic interactions or require time-consuming and expensive analysis. Microdialysis (MD) is emerging as a minimally invasive passive sampling method in environmental science, permitting the determination of solute fluxes and concentrations at previously unobtainable spatial scales and time frames. This article presents the first use of MD coupled to high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS) for the continuous sampling and simultaneous detection of CrVI in soil solution. The performance criteria of the system were assessed using stirred solutions; good repeatability of measurement (RSD < 2.5%) was obtained for CrVI, with a detection limit of 0.2 μg L-1. The online MD-HPLC-ICP-MS setup was applied to the sampling of native CrVI in three soils with differing geochemical properties. The system sampled and analyzed fresh soil solution at 15 min intervals, offering improved temporal resolution and a significant reduction in analysis time over offline MD. Simple modifications to the chromatographic conditions could resolve additional analytes, offering a powerful tool for the study of solute fluxes in soil systems to inform research into nutrient availability or soil-to-plant transfer of potentially harmful elements.

Multi-Element Determination of Trace Elements in B7-diesel Oil by High-Resolution Continuum Source Flame Atomic Absorption Spectrometry

In this work, a method for sequential multi-element determination of Cu, Fe, Ni, Pb, Zn, Al, Cr and Sn in B7-diesel oil samples by high-resolution continuum source flame atomic absorption spectrometry (HR-CS F AAS) was proposed. The sample preparation was based on formation of microemulsion (ME), which was investigated through a three-phase diagram. The ME composition adopted was B7-diesel oil (3.3 g), aqueous phase containing nitric acid (1.4 mol L-1, 300 μL) and n-propanol (up to 10 mL). The use of surfactant Triton X-100 in the formation of ME was also evaluated. Limits of detection in the range of 0.01 – 0.4 mg kg-1 were obtained. Spike-recovery tests were accomplished and the results varied between 93 and 124%. The accuracy of the proposed method was confirmed by the analysis of certified reference material (CRM) NIST SRM 1084a (Wear-Metals in Lubricating Oil); there was no statistical difference between the obtained results and the certified values (at 95% confidence level). Twelve B7-diesel oil samples were analyzed and Cu, Fe and Cr were found in three samples. The proposed method was simple, fast and accurate. The sequential multi-element determination of trace elements presented advantages as low costs and reduction in analysis time, being appropriate for routine analysis.

Mixed Magnetic Dispersive Micro‐Solid Phase–Cloud Point Extraction of Sunset Yellow in Food and Pharmaceutical Samples

AbstractThe simple and green separation/pre‐concentration technique combining magnetic dispersive micro‐solid phase extraction (MD‐μ‐SPE) with cloud point extraction (CPE) was applied for the trace determination of sunset yellow FCF (SY). Reduced graphene oxide‐cobalt oxide nanocomposite (RGO‐Co3O4) with high extraction ability as a suitable adsorbent was synthesized and characterized. In the proposed method, rapid and reproducible extraction is accomplished, lending reduced analysis time and relative inertness to surfactant concentration and equilibration temperature compared to conventional CPE. The influences of solution pH, concentration of sunset yellow, amount of RGO‐Co3O4, and triton X‐35 surfactant (10 % w/v) volume on the extraction performance of SY were investigated and optimized by multivariate optimization strategy. The proposed method, combining two different and effective techniques, offers satisfactory analytical features in terms of the limit of detection (9 ng mL−1), repeatability (1.8 %), and accuracy (89–102 %). Furthermore, the MD‐μ‐SPE/CPE method showed a good ability to analyze SY in food and pharmaceutical samples.

A Novel Meibomian Gland Morphology Analytic System Based on a Convolutional Neural Network

Meibomian glands dysfunction (MGD) is the main cause of dry eyes. Biological parameters of meibomian gland (MG) such as height, tortuosity and the degree of atrophy are closely related to its function. However, Thus, an effective quantitative diagnostic tool is needed for clinical diagnosis. Automatic quantification of MGs' morphological features could be a challenging task and play an important role in MGD diagnosis and classification. Our main objective is to develop an artificial intelligence (AI) system for evaluating MGs' morphology and explore the relationship between the morphological parameters and functions. We proposed a novel MGs extraction method based on convolutional neural network (CNN) with enhanced mini U-Net. A prospective study was conducted, 120 subjects were included and taken meibography. The training and validation sets encompassed 60 subjects; and the test set consisted of other 60 subjects with comprehensive examinations for ocular surface disease index questionnaire (OSDI), tear meniscus height (TMH), tear break-up time (TBUT), corneal fluorescein staining (CFS), lid margin score, and meibum expressibility score. The algorithm effectively extracted MGs from meibography even with this small training sample. As a result, while the intersection over union (IoU) achieved 0.9077, the repeatability was 100%. The processing time for each image was 100ms. Using this method, the investigators identified a significant and linear correlation between MG morphology and clinical parameters. This study provided a new method for quantification of MGs' morphological features obtained by meibography, which has advantages in reducing analysis time, improving diagnostic efficiency, and assisting ophthalmologists with limited clinical expertise.

Effective Spare Line Allocation Built-in Redundancy Analysis With Base Common Spare for Yield Improvement of 3D Memory

The memory cell density and memory capacity have been increased for obtaining larger and faster memory. However, this threatens the memory reliability by increasing the probability of faulty cell generation. In this paper, a fast and small-area built-in redundancy analysis (RA) for the post-bond repair process in 3D memory is proposed. Spare line allocation is the structure for memory repair which the most widely used. However, the spare line structure that replaces the entire line which contains faults in each memory layer occurs inefficiency in repairing a small number of faults, including single faults, and it is difficult to share spares between layers. The proposed idea is to use spare line to repair line faults according to the characteristics of it, and to complement the structural limitations by proposing a new redundant spare. This is base common spare located on the base die, and it can be shared across all layers. The proposed BIRA improves the efficiency of spare lines with two complementary spare resource structures, achieving a short repair time and high repair rate. The proposed BIRA achieves 100% repair rate when there are at least 2 times more faults than the previous works with the same hardware overhead. In addition, it takes only at most 80% of the analysis time compared to the previous works, and as the number of faults increases, the analysis time reduction becomes greater compared to the previous works.

Clinical trials with drugs in Brazil: an analysis of the main characteristics

Introdução: Os resultados de ensaios clínicos são utilizados pelas agências regulatórias de todo o mundo para fins de registro e comercialização de medicamentos. A Agência Nacional de Vigilância Sanitária (Anvisa) é a responsável pelos registros de tecnologias em saúde no Brasil e regras para análises técnicas de ensaios clínicos. A Anvisa vem atuando para atualizar seu arcabouço regulatório a respeito de ensaios clínicos com medicamentos no país, para reduzir tempo de análise e harmonizar conforme regras internacionais. Objetivo: Caracterizar os ensaios clínicos de fase III, com medicamentos, realizados no Brasil a partir da publicação da RDC n° 9, de 20 de fevereiro de 2015, da Anvisa. Método: Estudo exploratório e descritivo realizado em três etapas: (1) análise quantitativa pré e pós RDC n° 9/2015; (2) análise da população participante de ensaio clínico que embasou registros de medicamentos; (3) caracterização dos ensaios clínicos realizados no Brasil. Resultados: Houve redução em 20% de ensaios clínicos realizados no Brasil quando se compara o período anterior e posterior à publicação da RDC n° 9/2015 da Anvisa; apenas 33% dos ensaios clínicos que embasaram registros de medicamentos no país foram realizados com população brasileira; os medicamentos sintéticos e biológicos somam 96% da intervenção estudada nos ensaios clínicos; placebo ainda é muito utilizado como comparador (37%); a indústria farmacêutica é majoritariamente o patrocinador dos ensaios clínicos (86%). Conclusões: Diante deste cenário, é imperativo que se fortaleçam as ações de farmacovigilância no Brasil, a fim de conhecer os perfis de efetividade e segurança dos medicamentos após exposição da população brasileira.

Water-Soluble Carbon Quantum Dots Modified by Amino Groups for Polarization Fluorescence Detection of Copper (II) Ion in Aqueous Media

Industrialization is serious for changing the environment and natural water composition, especially near cities and manufacturing areas. Logically, the new ultrasensitive technology for precise control of the quality and quantity of water sources is needed. Herein, an innovative method of polarization fluorescence analysis (FPA) was developed to measure the concentration of heavy metals in water. The approach was successfully applied for precise tests with reduced analysis time and increased measurement efficiency among laboratory methods. Based on this work, the investigations established the new type of carbon quantum dots (CQDs) with controllable fluorescence properties and functionalized amino—groups, which is appropriate for FPA. The parameters of one and two-step microwave synthesis routes are adjusted wavelength and fluorescence intensity of CQDs. Finally, under optimized conditions, the FPA is showed the detection of copper (2+) cations in water samples below European Union standard (2 mg/L). Moreover, in comparison with fluorescence quenching, polarization fluorescence is proved as a convenient, simple, and rapid test method for effective water safety analysis.

Machine Learning-Based Flow Cytometry Diagnostics in Myelodysplastic Syndromes: Validation in the HOVON89 Clinical Trial (EudraCT 2008-002195-10)

Introduction Flow cytometry is a recommended tool in the diagnostic work-up of cytopenic patients suspected for myelodysplastic syndromes. Currently used flow cytometry scores rely on human assessment of dysplastic features in the bone marrow. Although proven useful, these methods are labor intensive and require a high level of expertise. Therefore, we previously developed a machine learning-based workflow for flow cytometry diagnostics in MDS by combining computational cell detection and a machine learning-classifier. This workflow outperformed traditional diagnostic scores with respect to accuracy (sensitivity 85-97%, specificity 93-97%), time investment (&amp;lt;30 seconds) and required materials (manuscript submitted). In the present study, we validated sensitivity of the workflow in a well-characterized clinical trial cohort (HOVON89 EudraCT 2008-002195-10) of lower risk MDS patients. Method Patient inclusion and characteristics Very low to intermediate risk MDS patients enrolled in the HOVON89 clinical trial (EudraCT 2008-002195-10) were included. 53 patients met the additional inclusion criteria, concerning written consent for add-on studies and availability of required flow cytometry data. Sample preparation Bone marrow samples were processed for flow cytometry analysis according to the European Leukemia Net guidelines. This study focused on the antibody combination optimized for assessment of myeloid progenitors and erythroid dysplasia (CD45, CD34, CD117, HLA-DR, CD71, CD36, CD105, CD33, sideward light scatter (SSC) and forward light scatter (FSC)). Machine learning-based workflow The machine learning-based workflow was developed in a prior study based on a reference cohort consisting of MDS patients without excess of blasts(n=67) and non-MDS cases (n=81) (Figure 1). MDS patients were diagnosed based on (cyto)morphology, cytogenetics and clinical follow-up. Non-MDS cases were patients with confirmed non-neoplastic cytopenias (n=69) and age-matched healthy individuals (n=12). Results In the validation cohort, the machine learning-based diagnostic workflow classified 49 out of 53 patients correctly, reaching a sensitivity of 92%. The workflow outperformed two currently used diagnostic tools for MDS flow cytometry, the Ogata score and integrated flow cytometry score (iFS). The former obtained 72% sensitivity (McNemar: p = 0.001) and the latter 83% sensitivity (McNemar: p = 0.06) in the validation cohort. Per patient, time required for automated analysis was less than 30 seconds. All four MDS patients that classified false negatively had a normal karyotype and (very) low risk disease according to the IPSS-r. In three out of four patients, no mutations or MDS-associated immunophenotypic features were detected. One patients was diagnosed as MDS-MLD and three patients as MDS-RS-SLD according to the WHO 2016 classification. The ten most relevant cellular features that discriminated between MDS and non-MDS patients in the reference data were confirmed in the current validation cohort. All ten features of MDS patients in the validation cohort were significantly different from non-MDS patients of the reference cohort (all features, p &amp;lt; 0.00001) (Figure 2). Seven out of ten features were similar in MDS patients of the validation cohort compared to those of the MDS patients of the reference cohort (p&amp;gt;0.05) (Figure 2). Conclusion In this validation study, we confirmed accuracy of machine learning-based flow cytometry diagnostics in lower risk MDS. The workflow obtained 92% sensitivity, which is in accordance with results from our previous study (85-97%), and outperformed currently used diagnostic flow cytometry scores for MDS (i.e. Ogata score and iFS). In our previous study specificity was 95% in both reference and test cohorts. Cellular features, most discriminative for diagnosis, were confirmed in the validation cohort, emphasizing robustness of the method. Additional benefits of this approach are the reduction in analysis time to less than thirty seconds per patient, reduction of required antibodies and increased reproducibility. Disclosures van de Loosdrecht: celgene: Honoraria; novartis: Honoraria.

Internet of things forensic data analysis using machine learning to identify roots of data scavenging

In this paper, we proposed the blockchain-assisted shared audit framework (BSAF) to analyze digital forensic data in the IoT platform. The proposed framework was designed to detect the source/cause of data scavenging attacks in virtualized resources (VR). The proposed framework implements blockchain technology for access log and control management. Access log information is analyzed for its consistency of adversary event detection using logistic regression (LR) machine learning and cross-validation. An adversary event detected by LR is filtered using cross-validation to retain the precision of data analysis for varying user density and VRs. Experimental results prove the consistency of the proposed method by improving the data analysis, as well as reducing analysis time and the adversary event rate.

Detection limits are central to improve reporting standards when using Nile red for microplastic quantification

Beyond simple identification of either the presence or absence of microplastic particles in the environment, quantitative accuracy has been criticised as being neither comparable nor reproducible. This is, in part, due to difficulties in the identification of synthetic particles amidst naturally occurring organic and inorganic components. The fluorescent stain Nile red has been proposed as a tool to overcome this issue, but to date, has been used without consideration of polymer specific fluorescent variability. The aim of this study was to evaluate the efficacy of Nile red for microplastic detection by systematically investigating what drives variations in particle pixel brightness (PPB). The results showed that PPB varied between polymer type, shape, size, colour and by staining procedure. Sand, an inorganic component of the sample matrix does not fluoresce when stained with Nile red. In contrast the organic components, wood and chitin, fluoresce between 1.40 and 12 arbitrary units (a.u.) and 32 and 74 a.u. after Nile red staining, respectively. These data informed the use of a PPB threshold limit of 100 a.u., which improved the detection of EPS, HDPE, PP and PA-6 from the 6 polymers tested and reduced analysis time by 30–58% compared to unstained samples. Conversely, as with traditional illumination, PET and PVC were not accurately estimated using this approach. This study shows that picking a threshold limit is not arbitrary but rather must be informed by polymer specific fluorescent variability and matrix considerations. This is an essential step needed to facilitate comparability and reproducibility between individual studies.

Multiblock Analysis Applied to TD-NMR of Butters and Related Products

This work presents a novel and rapid approach to predict fat content in butter products based on nuclear magnetic resonance longitudinal (T1) relaxation measurements and multi-block chemometric methods. The potential of using simultaneously liquid (T1L) and solid phase (T1S) signals of fifty samples of margarine, butter and concentrated fat by Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Selective Covariance Selection (SO-CovSel) methods was investigated. The two signals (T1L and T1S) were also used separately with PLS and CovSel regressions. The models were compared in term of prediction errors (RMSEP) and repeatability error (σrep). The results obtained from liquid phase (RMSEP ≈ 1.33% and σrep≈ 0.73%) are better than those obtained with solid phase (RMSEP ≈ 5.27% and σrep≈ 0.69%). Multiblock methodologies present better performance (RMSEP ≈ 1.00% and σrep≈ 0.47%) and illustrate their power in the quantitative analysis of butter products. Moreover, SO-Covsel results allow for proposing a measurement protocol based on a limited number of NMR acquisitions, which opens a new way to quantify fat content in butter products with reduced analysis times.

Rapid analysis of polycyclic aromatic hydrocarbons.

Polycyclic aromatic hydrocarbons are a continuing environmental and health concern. The analytical methods developed to analyze this class of compounds have relied on reversed phase liquid chromatography and are often on the order of tens of minutes. Reduction in analysis times through the application of sub-2 μm fully porous and superficially porous support materials can increase the throughput of these LC separations. Herein, we demonstrate similar selectivity between a fully porous 1.8 μm and a 2.7 μm superficially porous material. Separations were individually developed with in silico modeling for a given flow rate determined by the fully porous column's backpressure requirements. Since the 2.7 μm superficially porous materials inherently require less backpressure to achieve similar levels of efficiency as the 1.8 μm fully porous materials, a marked increase in throughput is possible with elevated flow rates. Good resolution for a standard 16-component sample mixture is demonstrated in a sub-minute separation.

Microfluidic platform for integrated plasmonic detection in laminal flow

In this work, we propose a novel approach to design robust microfluidic devices with integrated plasmonic transducers allowing portability, reduced analysis time through dynamic measurements and high sensitivity. Specifically, the strategy we apply involves two steps: (i) the controlled deposition of gold bipyramidal nanoparticles (AuBPs) onto a functionalized solid glass substrate and (ii) the integration of the as-fabricated plasmonic substrate into a polydimethylsiloxane (PDMS) microfluidic circuit. The localized surface plasmon resonance (LSPR) sensitivity of the plasmonic-microfluidic device was evaluated by monitoring the optical responses at refractive index changes, proving a bulk sensitivity of 243 nm RIU−1 for the longitudinal LSPR band of isolated AuBPs and 150 nm RIU−1 for the band assigned to end-to-end linked nanoparticles. A strong electric field generated in the gaps between AuBPs—due to the generation of the so-called extrinsic ‘hot-spots’—was subsequently proved by the volumetric surface enhanced Raman scattering (SERS) detection of molecules in continuous flow conditions by loading the analyte into the microfluidic channel via a syringe pump. In conclusion, our miniaturized portable microfluidic system aims to detect and identify, in real-time with high accuracy, analyte molecules in laminal flow, thus providing a groundwork for further complex biosensing applications.

Current Trends in Simultaneous Determination of Co-Administered Drugs

Recently, high demand of high-throughput analyses with high sensitivity and selectivity to molecules and drugs in different classes with different physical-chemical properties—and a reduction in analysis time—is a principal milestone for novel methodologies that researchers are trying to achieve—especially when analytical procedures are applied to clinical purposes. In addition, to avoid high doses of a single drug that could cause serious side effects, multi-drug therapies are often used to treat numerous diseases. For these reasons, the demand for methods that allow the rapid analysis of mixed compounds has increased in recent years. In order to respond to these needs, new methods and instruments have been developed. However, often the complexity of a matrix can require a long time for the preparation and processing of the samples. Different problems in terms of components, types of matrices, compounds and physical-chemical complexity are encountered when considering drugs association profiles for quantitative analyses. This review addresses not only recently optimized procedures such as chromatographic separation, but also methods that have allowed us to obtain accuracy (precision and trueness), sensitivity and selectivity in quantitative analyses for cases of drug associations.

Determination of citalopram in fish brain tissue: benefits of coupling laser diode thermal desorption with low- and high-resolution mass spectrometry.

Recent state-of-the-art methods developed for the analysis of polar xenobiotics from different types of biological matrices usually employ liquid chromatography with mass spectrometry. However, there are limitations when a small amount of sample mass is available. For example, individual benthic invertebrates or fish tissue samples often weigh less than 100mg (e.g., brain, liver) but are necessary to understand environmental fate and bioaccumulation dynamics. We developed ultra-fast methods based on a direct sample introduction technique. This included coupling laser diode thermal desorption with atmospheric pressure chemical ionization mass spectrometry (LDTD-APCI-MS). We then quantitated a common selective serotonin reuptake inhibitor (citalopram) in brain tissues of individual juvenile fish after in vivo exposure to environmentally relevant concentration. Two mass spectrometric methods based on low (LDTD-APCI-triple quadrupole (QqQ)-MS/MS) and high (LDTD-APCI-high-resolution product scan (HRPS)) resolutions were developed and evaluated. Individual instrument conditions were optimized to achieve an accurate and robust analytical method with minimum sample preparation requirements. We achieved very good recovery (97-108%) across the range of 1-100ngg-1 for LDTD-APCI-HRPS. LDTD-APCI-QqQ-MS/MS showed poorer performance due to interferences from the matrix at the lowest concentration level. LDTD-APCI ionization was successfully validated for analysis of non-filtered sample extracts. Evaluation of final methods was performed for a set of real fish brain samples, including comparison of LDTD-APCI-HRPS with a previously validated LC-heated electrospray ionization-HRPS method. This new LDTD-APCI-HRPS method avoids the chromatographic step and provides important benefits such as analysis of limited sample masses, lower total sample volume (typically μL), and reduction in analysis time per sample run to a few seconds. Graphical abstract.

Energy-Efficient Eco-Friendly Zero-Holding-Energy Magnetic Contactor for Industrial and Vehicular Applications

This paper reports an energy-efficient permanent magnet (PM) zero-holding-energy magnetic contactor (ZMC) that consumes no energy while maintaining an open or closed state. Conventional solenoid magnetic contactor (MC) technology has several critical drawbacks, including high continuous energy consumption, continuous vibration, and low reliability. The proposed ZMC substantially reduces energy consumption and heat generation, produces no vibration, and provides increased reliability. Traditional MC analysis typically requires computationally expensive finite element analysis (FEA). To reduce the analysis costs, a recently proposed fast and accurate nonlinear transient path energy method (NT-PEM) is utilized to significantly reduce analysis time and costs. Analysis accuracy was verified experimentally using a fabricated ZMC prototype. The economic and environmental benefits of the proposed ZMC confirm its significant potential as an energy-efficient eco-friendly alternative to traditional MC technology. ZMC is particularly promising for vehicular micro-grids due to the prolongation of the lifetime of batteries owing to reduced energy consumption.

High-Speed Capillary Electrophoresis Using a Thin-Wall Fused-Silica Capillary Combined with Backscatter Interferometry.

High-speed capillary electrophoresis (HSCE) is implemented using a 10 cm total length fused-silica capillary (50 μm i.d., 80 μm o.d.) combined with refractive index (RI) detection using backscatter interferometry (BSI). The short capillary length reduces analysis time while the ultrathin wall (15 μm) efficiently dissipates heat from the separation channel, mitigating the deleterious effects of Joule heating. The separation capillary is mounted on a temperature-controlled heat sink that stabilizes the temperature to ±0.004 °C. This temperature stabilization improves separation efficiency and enhances RI detection. Ohm's Law plots confirm the superior heat dissipation of the HSCE capillary compared to a similarly prepared conventional CE capillary (50 μm i.d., 363 μm o.d.). The speed and efficiency of HSCE combined with universal RI detection is illustrated through the separation of K+, Ba2+, Mg2+, Na+, Li+, and Tris+ in approximately 30 s, with efficiencies greater than 500 000 plates/m. Run-to-run repeatability is explored using nine consecutive electrokinetic injections of a K+, Na+, and Li+ mixture. The average migration times and %RSD for K+, Na+, and Li+ were measured to be 22.04 s (1.59%), 26.81 s (1.38%), and 29.80 s (2.21%), respectively. Finally, we show that the BSI signal is sensitive to the separation voltage through the Kerr mechanism. This leads to peaks in the electropherogram from the injection process that are useful for precisely defining the start of each separation and quantifying the amount of sample injected onto the capillary.