Quantities Of Samples Research Articles

A Comparative Elemental Analysis of Espresso Coffee from Poland and Portugal

A comparative elemental analysis of espresso coffee from Poland and Portugal was carried out. Using an ICP-MS analytical procedure, samples collected from public cafes in Poland and Portugal (n = 60 and n = 44, respectively) were studied for their macromineral and trace element content. To evaluate the contribution of water to the final composition of the beverage, paired samples (i.e., collected from the same locations) of drinking water were also analysed. The mineral profile of the coffee espresso samples was quite similar: Mg > P > Ca > Rb > Mn > B > Zn > Cu > Sr > Ba > Ni > Pb > Cs > Mo > Sn > Cd > Sb > Tl for samples from Poland and Mg > P > Ca > Rb > B > Mn > Zn > Sr > Cu > Ni > Ba > Cs > Pb > Mo > Sn > Sb > Cd > Tl for samples from Portugal. For most of the elements, the espresso samples showed much higher levels than the water used in its preparation. The two most notable exceptions were Ca and Sr, where the elements present in the coffee came mainly from the water. The contribution of coffee espressos to the daily intake of essential elements seems to be reduced. Other non-essential elements like Ni (median = 81.0 µg/L and 86.8 µg/L for Polish and Portuguese espresso, respectively) and Pb (median = 14.3 µg/L and 4.43 µg/L, respectively) were observed in significant amounts in the coffee espresso samples analysed in this study. These elements have been shown to leach from coffee machines in other studies. More studies are necessary to confirm these results.

Determination of montelukast and non-sedating antihistamine combination in pharmaceutical dosage forms: A review

Abstract Combining non-sedating antihistamines (NSAs) with montelukast (MON) has been found to significantly enhance the therapeutic efficacy against daytime and composite nasal symptoms, including rhinorrhea, sneezing, and itching. This article reviews the current analytical methods employed for the identification and quantitative determination of MON in combination with NSAs in various marketed formulations. The most commonly used methods for the determination of MON and NSAs in combination are chromatographic methods (high-performance liquid chromatography [HPLC] and thin-layer chromatography) and spectrometry methods (spectrofluorometry and spectrophotometry). Recent preferences in the analysis of MON and NSAs in combination samples prove the primacy of HPLC (61%) and confirm the general trends moving toward more sensitive methods, with a higher resolution potential, consumption of small quantities of samples and reagents, and requiring less analysis time.

Biochemical Oxygen Demand Prediction Based on Three-Dimensional Fluorescence Spectroscopy and Machine Learning

Biochemical oxygen demand (BOD) is an important indicator of the degree of organic pollution in water bodies. Traditional methods for BOD5 determination, although widely used, are complicated and dependent on accurate chemical measurements of dissolved oxygen. The aim of this study was to propose a facile method for predicting biochemical oxygen demand by fluorescence signals using three-dimensional fluorescence spectroscopy and parallel factor analysis in combination with a machine learning algorithm. The water samples were incubated for five days using the national standard method, during which the dissolved oxygen contents and three-dimensional fluorescence spectroscopy data were measured at eight-hour intervals. The maximum fluorescence intensity of three fluorescence components was decomposed and extracted by parallel factor analysis. The relationship between the maximum fluorescence of the three fluorescence components and the BOD5 values was established using a random forest model. The results showed that there was a good correlation between the fluorescence components and BOD values. The BOD5 values were effectively predicted by the random forest model with a high goodness of fit (R2 = 0.878) and low mean square error (MSE = 0.28). Although this method did not shorten the incubation time, successful BOD5 prediction was realized by the non-contact measurement of fluorescence signals. This avoids the complicated operation of DO determination, improves detection efficiency, and provides a convenient solution for analyzing large quantities of water samples and monitoring facile water quality.

Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network.

This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time-frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time-frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components. This strategy improves both the time-frequency aggregation and the resolution, which better reflects the eigenvalues of non-stationary signals. In this process, transfer learning and a residual structure are used in the training of a convolutional neural network. The resulting time-frequency diagrams, acquired using the steps discussed above, are then input to the TRCNN for diagnosis. A series of validation experiments confirmed that applying the TRCNN structure made it possible to achieve high diagnostic accuracy, even when training the network with only a small number of fault samples, as all 12 fault types from the test dataset were diagnosed correctly. Further simulation experiments demonstrated that our proposed method improved fault diagnosis accuracy compared to that of conventional techniques (with increases of 1.74% over RCNN, 1.28% over TCNN, 1.62% over STFT, 1.73% over WT, 2.83% over PWVD, and 1.39% over STFA-PD). In addition, diagnostic accuracy reached 100% during the application of three-time transfer learning, validating the effectiveness of the proposed method for rolling bearing fault diagnosis.

Differential DNA Extraction from Lateral Flow Immunochromatographic Tests via the EZ1® Advanced XL System.

This differential extraction protocol details the steps for isolating DNA from sample pads used in lateral flow immunochromatographic (LFI) tests, particularly for cases involving mixed biological samples such as semen and menstrual blood, or other evidence related to sexual assault. This procedure utilizes a differential extraction technique applied to sample pads from immunochromatographic tests, where the sample pads serve as the substrate. The method involves two sequential lysis steps to effectively separate non-sperm and sperm fractions, enabling the targeted isolation of distinct cell types for downstream DNA analysis. The efficiency of this procedure is demonstrated by the results within this paper, which highlights the successful recovery of both male autosomal and Y-STR profiles, even in mixed samples with a high female presence. Overall, this protocol demonstrates the effective recovery of DNA from sample pads, which is beneficial for forensic practitioners dealing with limited sample quantities, underscoring the value of using these pads in forensic analysis.

Process parameters settings and quality investment selection for modified traditional inventory model

In this article, we propose the process parameters settings in the first phase. Then the extended problem with production quantity, quality investment, and parameters of acceptance sampling in the second phase. Taguchi’s loss cost is used for evaluating quality.

Advancements in Magnetic Nanoparticle Design: SiO2@Fe3O4 Core/Shell Nanoparticles with Size-Tunable Magnetic Responses

Thin magnetic films conformed to closed surfaces are theoretically predicted to exhibit exotic magnetic ground states at microscopic curvatures. However, there is a general lack of experimental reports on the subject, mostly associated to challenges in achieving the desired geometry at the nanoscale. In this work, we tackle this issue by experimentally probing magnetite nanoshells grown on the surface of bare silica nanospheres of 20 nm – 600 nm in diameter. Such a system can be described as magnetic centers arranged in a closed, non-flat film. Results reveal that the absence of a magnetic core results in shells with a curvature-dependent magnetic response which is markedly different to that of conventional bulk magnetic nanoparticles. We report that a granular aspect in such magnetic nanoshells negatively affects the formation of clear vortex signatures in measurements of macroscopic sample quantities. Micromagnetic simulations suggest this to be the result of the pinning of vortices on different regions of the sphere surface, leading to sharp, field-driven variations of the magnetic response of individual particles.

Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection

The need for precise identification of underwater sonar image targets is growing in areas such as marine resource exploitation, subsea construction, and ocean ecosystem surveillance. Nevertheless, conventional image recognition algorithms encounter several obstacles, including intricate underwater settings, poor-quality sonar image data, and limited sample quantities, which hinder accurate identification. This study seeks to improve underwater sonar image target recognition capabilities by employing deep learning techniques and developing the Multi-Gradient Feature Fusion YOLOv7 model (MFF-YOLOv7) to address these challenges. This model incorporates the Multi-Scale Information Fusion Module (MIFM) as a replacement for YOLOv7’s SPPCSPC, substitutes the Conv of CBS following ELAN with RFAConv, and integrates the SCSA mechanism at three junctions where the backbone links to the head, enhancing target recognition accuracy. Trials were conducted using datasets like URPC, SCTD, and UATD, encompassing comparative studies of attention mechanisms, ablation tests, and evaluations against other leading algorithms. The findings indicate that the MFF-YOLOv7 model substantially surpasses other models across various metrics, demonstrates superior underwater target detection capabilities, exhibits enhanced generalization potential, and offers a more dependable and precise solution for underwater target identification.

Advanced Design Validation Planning Techniques for Commercial Vehicle E-Axles

<div>Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant.</div> <div>Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in the e-axle design validation plan (DVP), particularly in relation to the start of production (SOP) decision-making quality gates.</div> <div>Many DVPs incorporate tests in a specific order to enhance the validation impact of each sample and to guarantee that preconditioning tests are implemented when necessary. An instance of preconditioning is subjecting a system to thermal cycling. Thermal cycle preconditioning ensures that the sample accurately represents a real-world system when undergoing sealing-related tests, such as ingress protection testing according to ISO 20653 [<span>1</span>]. Assessing the necessary thermal cycling to “bed-in” a sample would be highly elaborate using a physical sample, but it is a straightforward aspect to extract from modern finite element analysis (FEA). A case study will be presented to evaluate the minimum required thermal cycling and determine the cost savings for the DVP.</div> <div>Furthermore, the validation supported by simulations will be showcased in the context of accelerated lifetime testing. Often, accelerating such tests necessitates a balance between the distribution of damages and the overall testing duration. By incorporating safety factors from simulations as an initial input, the distribution of damages can be adjusted to allow for some over-testing on more secure components, thereby reducing the overall testing time.</div> <div>Efficiency assessment will additionally be considered for virtual validation due to the complexity of the measurement and data acquisition, contributing to measurement uncertainty. Furthermore, simulation-based validation will be considered for the case of lubrication testing, which can be hampered by a largely qualitative assessment criteria.</div> <div>Overall, these scenarios will be evaluated for their potential to streamline the overall DVP process in terms of testing time, sample quantity, and critical path duration. This underscores the potential benefits of leveraging advanced DVP techniques for cost and time savings.</div>

MiRNA in archival serum samples derived from breast cancer patients treated with neoadjuvant chemotherapy vs freshly collected samples: Pilot study.

Liquid biopsy, including miRNA profiling, is a promising approach to identify breast cancer (BC) resistance. However, the effect of long-term storage on the quality of miRNA assessment in archival serum has not been fully addressed. We aimed to determine whether miRNAs were recoverable from long-stored serum samples to subsequently evaluate prognostic and predictive miRNA value in the archival collection of samples from patients treated with neoadjuvant chemotherapy at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice, Poland. We have evaluated miRNA quantity in serum samples stored for up to 12 years. Additionally, we compared miRNA expression in archival samples to freshly collected samples derived from advanced BC patients. Forty BC patients were included in the study. Archival samples were derived from 20 BC patients treated with radical intent between 2011 and 2015. Freshly collected samples were collected from 20 advanced BC patients in 2022. miRNA was present in archived serum samples frozen at -80C° for at least 12 years. Additionally, we found significantly different expressions between the 2 analyzed groups. Expression of circulating miR-16, -17, -18a, -20a, -21, -27a, -30b, -222, and -326 were significantly higher in archival samples, whereas expression of circulating miR-19a, -29b, -29c, -128, -145, -146a, -193b, -195, -200b, -210, -221, -424, and -451a were lower than in freshly collected samples. In 14 miRs, we observed expression in both groups; however, differences were statistically insignificant (miR-1, -7a, -10b, -19b, -34a, -99a, -106b, -122, -125b, -155, -200a, -205, -223, -340). MiRNA can be identified from long-stored samples, making large prospectively collected serum repositories with long follow-up time an invaluable source for miRNA biomarker discovery.

Enantiomerically Pure Helical Bilayer Nanographenes: A Straightforward Chemical Approach.

The semiconductor properties of nanosized graphene fragments, known as molecular nanographenes, position them as exceptional candidates for next-generation optoelectronics. In addition to their remarkable optical and electronic features, chiral nanographenes exhibit high dissymmetry factors in circular dichroism and circularly polarized luminescence measurements. However, the synthesis of enantiomerically pure nanographenes remains a significant challenge. Typically, these materials are synthesized in their racemic form, followed by separation of the enantiomers using high-performance liquid chromatography (HPLC). While effective, this method often requires expensive instrumentation, extensive optimization of separation conditions, and typically yields analytical quantities of the desired samples. An alternative approach is the enantioselective synthesis of chiral molecular nanographenes; however, to date, only two examples have been documented in the literature. In this work, we present a straightforward chemical method for the chiral resolution of helical bilayer nanographenes. This approach enables the effective and scalable preparation of enantiomerically pure nanographenes while avoiding the need for HPLC. The incorporation of a BINOL core into the polyarene precursor facilitates the separation of diastereomers through esterification with enantiomerically pure camphorsulfonyl chloride. Following the separation of the diastereomers by standard chromatographic column, the hydrolysis of the camphorsulfonyl group yields enantiomerically pure nanographene precursors. The subsequent graphitization, achieved through the Scholl reaction, occurs in an enantiospecific manner and with the concomitant formation of a furan ring and a heterohelicene moiety. The absolute configurations of the final enantiomers, P-oxa[9]HBNG and M-oxa[9]HBNG, have been determined using X-ray diffraction. Additionally, electrochemical, photophysical, and chiroptical properties have been thoroughly evaluated.

Determination of the pKa and Concentration of NMR-Invisible Molecules and Sites Using NMR Spectroscopy.

NMR spectroscopy is a very powerful tool for measuring the dissociation constants (pKa) of molecules, requiring smaller quantities of samples of lower purity relative to potentiometric or conductometric methods. However, current approaches are generally limited to those molecules possessing favorable pH-dependent NMR properties. Typically, a series of 1D experiments at varying pH are performed, and the pKa is obtained by fitting the observed chemical shift of the analyte as a function of pH using nonlinear routines. However, the majority of polymers, biomolecules, and inorganic species do not present favorable NMR resonances. Either the resonances are not observable or too broad, or the unambiguous interpretation of the NMR data is impossible without resorting to complex 2D experiments due to spectral overlap. To overcome these fundamental limitations, we present a method to obtain the pKa values and concentrations of acidic species without their direct observation by NMR. We instead determine the quantity of acidic protons removed from the species along a concentration gradient of an organic base in a single 1H chemical shift imaging experiment that can be run under automation. The pKa values are determined via simple linear plots, avoiding complex and potentially unreliable nonlinear fitting routines.

Comparison of laser diffractometry and pipetting methods for particle size determination: A pilot study on the implications of result discrepancies on soil classification

AbstractIn recent decades, the determination of particle size distribution (PSD) using the laser diffraction method (LDM) has become increasingly common, supplanting traditional sedimentation techniques. Advances in everything from sample preparation to software settings have been realized globally, whether through recommendations from laser diffraction (LD) manufacturers or through user experiences. These developments seek to enhance accuracy and diminish the uncertainties associated with new methodologies. Particularly in the determination of PSD using LDM on various LD instruments and in comparison with the sieve–pipette method (SPM), discrepancies in PSD frequently arise. This article aims to mitigate these discrepancies by predefining parameters, specifically through the adjustment of LD software settings and sample preparation (employing a uniform set of dispersed samples in potassium hydroxide) on two widely used LD instruments for soil measurements: Mastersizer 3000 and Analysette 22. Additionally, these samples were analyzed using the traditional SPM (ISO 11277, 1998), with the results from LDM and SPM subsequently compared. The paper also explores the impact, range of user options, and limitations of predefined software settings on both LD instruments. Eighty soil samples were analyzed for PSD, collected from arable land in the cadastral area of Hrušky, district of Břeclav (Czech Republic), in spring 2022, from depths of 0‐ to 10‐cm and 10‐ to 20‐cm. Significant differences in PSD were confirmed, although the trends of the grain size distribution curves were very similar to those of LDM. Results from the Mastersizer underestimated the clay fraction by an average of 17% compared to SPM, at the expense of the sand fraction, whereas the silt fraction was underestimated by a maximum of 4%. Conversely, Analysette 22 overestimated the silt fraction by an average of 37% at the expense of the sand fraction, confirming only a slight difference in the clay fraction: 3%. Moreover, the quantity of sample entering the dispersion unit was identified as a significant issue when comparing LD instruments, despite the obscuration value being nearly identical, that is, 20%–30%. Therefore, it was not possible to achieve the same or similar weight when introducing suspension into circulation. The robustness of the obtained results underscores the importance of understanding input parameters when interpreting results between different methods.

Potential role of bacterial extracellular vesicles in the pathophysiology of systemic lupus erythematosus

Systemic Lupus Erythematosus (SLE) is a rare autoimmune disorder influenced by various factors, including infections. Following bacterial or viral infections, there is an increase in IFN-I production, primarily by plasmacytoid dendritic cells (pDCs). Overproduction of IFN-I has been shown to drive the progression of SLE. Recent findings indicate that the gene expression signature of IFN-I in lupus nephritis patients does not co-occur with pDCs as expected; instead, it is localized in glomerular regions with anastomosing capillaries, suggesting a potential source from the blood. T cells, natural killer cells, and myeloid lineage cells may also secrete IFN in the bloodstream. Bacterial presence in the bloodstream challenges the concept of blood sterility, raising the possibility that cell-free microbial components, such as bacterial extracellular vesicles (BEVs), could trigger excessive IFN production through systemic circulation. While existing studies suggest a role for BEVs in human SLE, experimental evidence has yet to establish a direct association between the entire BEV nanoparticle and the disease. Research has primarily focused on (1) mammalian EVs and (2) purified eDNA and other specific cargoes as contributors to SLE progression. It remains unproven whether these bacterial molecules exert their effects while associated with vesicular membranes, i.e., BEVs. We hypothesize that BEVs, which carry diverse cargoes including nucleic acids, may enter systemic circulation, induce cellular responses leading to IFN overproduction, and exacerbate SLE severity. Clinical studies could investigate the association of BEVs with SLE progression by monitoring their presence and quantity in blood samples and correlating these factors with disease outcomes. Furthermore, understanding the involvement of BEVs may aid in identifying SLE biomarkers, inform infection prevention strategies, and inspire the development of BEV-neutralizing agents.

Screening for Microplastics in Agricultural Soils: Applying Green Chemistry Principles in Extraction and Analysis.

In recent years, microplastic (MP) pollution has garnered significant attention owing to its ability to permeate various ecosystems, including soil. These particles can infiltrate the environment, either directly or through the degradation of larger plastic items. Despite growing concerns, standardized methods for quantification are still lacking. This study aimed to screen for the presence of MPs in agricultural soils while incorporating green analytical principles in the methodology. A density separation followed by centrifugation was employed, based on the principles of the QuEChERS extraction method. This approach minimized sample quantities, reagent consumption, and waste production, ensuring efficient extraction and analysis. Recovery tests using certified soils spiked with pristine MPs, specifically polystyrene, polypropylene (PP), and ethylene-vinyl acetate for larger MPs (3-5 mm), and low-density polyethylene, polyamide 6, and tire wear particles for smaller MPs (15-300 μm),achieved recovery levels exceeding 69% for smaller MPs and over 91% for larger particles. Spectroscopic analysis revealed slight alterations in the Raman spectra of MPs after extraction. Transitioning to agricultural soil analysis has revealed challenges, including spectral interferences. Nine mesoplastics (plastic items 5-20 mm) were detected, predominantly consisting of PP and PE, along with seven MPs, three of which were individually identified as PE-based, while the remainder were inconclusive, including one fiber. The evaluation of the method's sustainability using the Analytical Eco-Scale and Analytical Greenness Calculator Metric (AGREE), with scores of 82 out of 100 and 0.66 out of 1, demonstrated its potential as a liable approach to MP analysis in soils. This study highlights the potential of integrating green analytical chemistry principles into MP extraction methodologies and emphasizes the value of the proposed QuEChERs-based approach for improving the sustainability and efficiency of MP monitoring in agricultural soils.

Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification

In Hyperspectral Image (HSI) classification, acquiring large quantities of high-quality labeled samples is typically costly and impractical. Traditional deep learning methods are limited in such scenarios due to their dependence on sample quantities. To address this challenge, researchers have turned to Few-Shot Learning (FSL). Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. Moreover, during domain alignment, features of different categories within the same domain may become confused. To address these issues, this paper proposes a novel Domain-Invariant Few-Shot Contrastive Learning (DIFSCL) method, which combines domain adaptation and contrastive learning strategies to not only learn domain-invariant features but also significantly enhance inter-class discriminability. Based on this, we further design a multi-scale adaptive attention mechanism for a hyperspectral feature extraction network to more effectively extract and optimize generalized features within the DIFSCL framework, significantly improving intra-class consistency and inter-class discriminative capability of features. Experimental results on three widely used HSI datasets demonstrate that our method significantly outperforms existing techniques in few-shot classification tasks.

Efficient generation of HPLC and FTIR data for quality assessment using time series generation model: a case study on Tibetan medicine Shilajit.

The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

Mapping Active RNA Polymerases in Proliferating and Quiescent Fission Yeast Cells Using Precision Run-On Sequencing.

The development of next-generation sequencing (NGS) approaches to investigate the functioning of RNA polymerases has led to groundbreaking advances in the field of transcriptional regulation. One powerful method, Precision nuclear Run-On sequencing (PRO-seq), maps the locations of RNA polymerase active sites genome-wide at high resolution. PRO-seq provides a snapshot of strand-specific transcriptional activity and does not rely on immunoprecipitation of the polymerase of interest. Notably, this technique has been utilized to investigate the control of the RNA polymerase II transcription cycle in a variety of model systems. However, the initially published PRO-seq method required significant amounts of starting sample and was technically challenging, both of which were deterrents for its broader use. Recently, an improved and simplified version called qPRO-seq that reduced the length of the experiment and the quantity of necessary input sample was developed for human and Drosophila cell lines. Here we provide an updated, step-by-step protocol in which we have validated and optimized qPRO-seq for the fission yeast Schizosaccharomyces pombe. Importantly, we have implemented this method for assessing RNA polymerase activity in nutrient-limiting conditions, for both proliferating and nitrogen-depleted quiescent cells.

Highly specific detection of ROR1 cancer biomarker with bipolar electrochemiluminescence.

An electrochemiluminescence (ECL) detection system is presented integrated with a bipolar electrode system for sensitive cancer diagnosis. In order to achieve the highest electrical conductivity and redox-active surface area, MXene was chosen as the material for the bipolar electrode. As part of the detection process, the anodic pole of the bipolar electrode was modified with the receptor tyrosine kinase like orphan receptor 1 (ROR1) antibody, followed by an immunoassay using the ROR1 antibody-modified Ag triangle that was identified as significantly enhancing ECL. We measured the ECL of luminol using the anode pole of BPE as an analytical signal in the presence of H2O2. Additionally, 3D-printed microchannels were used to fabricate the BPE system, to reduce the quantity of sample required. It has been shown that the present immunosensors are low-cost and sensitive in detecting types of cancer, with an extended linear range of 10 fg mL-1 to 1 µg mL-1 in the analysis of synthetic samples and achieving an accuracy of ~ 90% in diagnosing ten unknown real samples.

Ammonothermal Synthesis of Luminescent Imidonitridophosphate Ba4P4N8(NH)2:Eu2.

The structural variability of a compound class is an important criterion for the research into phosphor host lattices for phosphor-converted light-emitting diodes (pc-LEDs). Especially, nitridophosphates and the related class of imidonitridophosphates are promising candidates. Recently, the ammonothermal approach has opened a systematic access to this substance class with larger sample quantities. We present the successful ammonothermal synthesis of the imidonitridophosphate Ba4P4N8(NH)2:Eu2+. Its crystal structure is solved by X-ray diffraction and it crystallizes in space group Cc (no. 9) with lattice parameters a=12.5250(3), b=12.5566(4), c=7.3882(2) Å and β=102.9793(10)°. For the first time, adamantane-type (imido)nitridophosphate anions [P4N8(NH)2]8- are observed next to metal ions other than alkali metals in a compound. The presence of imide groups in the structure and the identification of preferred positions for the hydrogen atoms are performed using a combination of quantum chemical calculations, Fourier-transform infrared, and solid-state NMR spectroscopy. Eu2+ doped samples exhibit cyan emission (λmax=498 nm, fwhm=50 nm/1981 cm-1) when excited with ultraviolet light with an impressive internal quantum efficiency (IQE) of 41 %, which represents the first benchmark for imidonitridophosphates and is promising for potential industrial application of this compound class.