Unknown Samples Research Articles

Evaluation of genotypic variations in the protein content of soybean through near infrared spectroscopy

Demand for soybean seeds with increased protein content is high in the international market. In this context, breeding soybean varieties targeting high protein content is the need of the day. In this paper, elite soybean germplasm accessions were screened for protein content. Eighty-eight soybean germplasm was evaluated for protein content through near infrared spectroscopy. A prediction model to determine the protein content of soybean germplasm through a non-destructive method was developed to calibrate the NIR. The protein content of 39 lines was analyzed in wet lab conditions and used to calibrate in NIRs between 1400 and 2400 nm at 2 nm intervals. High determination coefficient and low values of root mean square error (2.585) and standard error of prediction (2.832) confirmed the model’s utility for predicting the protein content of unknown samples. Accordingly, the protein content of 49 germplasm lines revealed that the genotypes LU96, TNAU20056, and SL525 depicted high values of 56.66, 55.51, and 54.48% protein content, respectively, but with fewer yields. The genotype RKS45 recorded a protein content of 45.33% with a single plant yield of 34.09 g, which can be further utilized for hybridization and selection. Thus, a non-significant correlation was observed between the protein content and single plant yield, suggesting that an increase in protein content will not directly influence the yield parameters. This paper provides a simple methodology to accurately determine the protein in a large set of samples in a short time, which helps in speed breeding programs.

Machine Learning-Assisted Biomass-Derived Carbon Dots as Fluorescent Sensor Array for Discrimination of Warfarin and Its Metabolites.

Warfarin (WAR), an effective oral anticoagulant, is of utmost importance in treating many diseases. Despite its significance, rapid and precise discrimination of WAR remains a formidable challenge, especially facing its structural analogs of metabolites. Here, three kinds of herb-derived N-doped carbon dots (NCDs) were greenly synthesized via a fast and simple microwave-assisted method. Three NCDs showcased respectable blue fluorescent (FL) properties and sensing capabilities for the discrimination of WAR and its metabolites. To improve accuracy in identifying WAR and its metabolites, a sensor array composed of three unique herb-derived NCDs was meticulously designed. Combined with the machine learning model, the sensor array displayed a strong immunity to interference in the discrimination of the WAR, even in unknown samples. Meanwhile, the FL sensing mechanism is deeply expounded. The methodology proffers broad prospects for biomass-derived nanomaterials and provides an effective and feasible project for pharmaceutical analysis by capitalizing on machine learning.

Cosine Distance Loss for Open-Set Image Recognition

Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggles to improve recognition performance caused by open space risk. Therefore, we introduce a cosine distance loss function (CDLoss), which exploits the orthogonality of one-hot encoding vectors to align known samples with their corresponding one-hot encoder directions. This reduces the overlap between the feature spaces of KCs and UCs, mitigating open space risk. CDLoss was incorporated into both Softmax-based and prototype-learning-based frameworks to evaluate its effectiveness. Experimental results show that CDLoss improves AUROC, OSCR, and accuracy across both frameworks and different datasets. Furthermore, various weight combinations of the ARPL and CDLoss were explored, revealing optimal performance with a 1:2 ratio. T-SNE analysis confirms that CDLoss reduces the overlap between the feature spaces of KCs and UCs. These results demonstrate that CDLoss helps mitigate open space risk, enhancing recognition performance in open-set image classification tasks.

Standardized workflow for multiplexed charge detection mass spectrometry on orbitrap analyzers.

Individual ion mass spectrometry (I2MS) is the Orbitrap-based extension of the niche mass spectrometry technique known as charge detection mass spectrometry (CDMS). While traditional CDMS analysis is performed on in-house-built instruments such as the electrostatic linear ion trap, I2MS extends CDMS analysis to Orbitrap analyzers, allowing charge detection analysis to be available to the scientific community at large. I2MS simultaneously measures the mass-to-charge ratios (m/z) and charges (z) of hundreds to thousands of individual ions within one acquisition event, creating a spectral output directly into the mass domain without the need for further spectral deconvolution. A mass distribution or 'profile' can be created for any desired sample regardless of composition or heterogeneity. To assist in reducing I2MS analysis to practice, we developed this workflow for data acquisition and subsequent data analysis, which includes (i) protein sample preparation, (ii) attenuation of ion signals to obtain individual ions, (iii) the creation of a charge-calibration curve from standard proteins with known charge states and finally (iv) producing a meaningful mass spectral output from a complex or unknown sample by using the STORIboard software. This protocol is suitable for users with prior experience in mass spectrometry and bioanalytical chemistry. First, the analysis of protein standards in native and denaturing mode is presented, setting the foundation for the analysis of complex mixtures that are intractable via traditional mass spectrometry techniques. Examples of complex mixtures included here demonstrate the relevant analysis of an intact human monoclonal antibody and its intricate glycosylation patterns.

Chemical characterization of polymer and chloride content in waste plastic materials using pyrolysis - direct analysis in real time - high-resolution mass spectrometry.

The increasing global demand for plastic has raised the need for effective waste plastic management due to its long lifetime and resistance to environmental degradation. There is a need for rapid plastic identification to improve the mechanical waste plastic sorting process. This study presents a novel application of Temperature-Programmed Desorption-Direct Analysis in Real Time-High Resolution Mass Spectrometry (TPD-DART-HRMS) that enables rapid characterization of various plastics. This technique was applied on four commercially available reference polymers (polyethylene, polypropylene, polystyrene, polyvinyl chloride) as well as three "waste" plastic samples of mixed origin. These waste plastic samples were obtained as discards from various industrial processes with limited analytical characterization data. Through the application of CH2 Kendrick mass defect (KMD) grouping, characteristic trends in the mass spectra of each sample were identified, allowing for a simplified numerical comparison. This approach utilized a robust statistical approach using the Tanimoto coefficient, allowing for the quantitative measures of similarity between standards and unknown samples. The application of this mathematical evaluation methodology was used to identify plastic types and to distinguish structurally similar polymers. Additionally, we report that a chloride ion clustering effect with copper substrate can identify chlorinated polymer PVC (polyvinyl chloride) utilizing pyro-(-)DART-HRMS mode. PVC polymer is of particular interest in recycling due to its high chloride content, which can present technical challenges for some types of recycling. We found that chloride ion clusters are a good screening marker for the presence of chlorinated polymers in mixed waste plastic samples. This study can possibly help advance rapid and accurate analytical techniques for identifying the composition of waste plastics to advance the effectiveness of the waste plastic sorting process.

Evidence of occupational exposure: workplace discrimination based on ATR-FTIR analysis of fingernail clippings for forensic applications

For the first time, an attempt was made to differentiate the occupation of male workers from their fingernail clippings (80 samples aged between 20 to 60 years). ATR-FTIR spectroscopy coupled with statistical algorithms (PCA & PLS-DA) was utilized for characterization and discrimination purpose. ATR-FTIR spectral analysis depicted the presence of aldehyde, amine salt, amide I, II and III, Metal-O stretching and aliphatic & aromatic hydrocarbons in fingernail clippings, collected from different male occupants. Specifically, PCA and PLS-DA were used for exploratory and predictive data analysis. It was noticed that PCA just provided an indication of the number of distinct classes instead of classifying the data. On the other hand, PLS-DA has been found to be excellent for predicting the workplace on the basis of classifying male fingernail clippings from different occupational exposure. Using 10 unknown samples, the model was further validated, and each unknown sample’s prediction was found to be correct, yielding a 100% accuracy rate. When fingernail clippings are discovered at a crime scene as corroborative evidence, the findings of this study may be useful to a forensic expert. Furthermore, it aids in focusing and narrowing down the criminal inquiry depending on occupation. For this purpose, a database based on forensic examination of fingernail clippings could be created, for workplace discrimination based on occupational exposure.

Open-set fault diagnosis based on dynamic triple multivariate guided structural constraints

Abstract Existing deep learning-based models for mechanical fault diagnosis perform well in identifying predefined faults, but these models substantially degrade in performance when they encounter unknown faults. Thus, it is crucial to investigate open-set fault diagnosis that can handle unknown faults more efficiently. Current methods for open-set fault diagnosis in machinery face challenges by the lack of hierarchical structure in feature representation and the overlapping regions of known and unknown sample distributions. To solve these problems, we propose a composite dual-branching dynamic triplet multivariate constrained (CDDTMC) model for mechanical open-set fault diagnosis. The CDDTMC framework consists of three main core modules: a feature extraction module, a structural constraint module and a fault diagnosis module. In the feature extraction module a composite two-branch network is designed to extract hierarchical feature representations from known samples. After extracting the sample features, it represents the samples with structural constraints using multivariate constraints based on bidirectional dynamic triplet loss to achieve discriminativeness and compactness. Determining the optimal decision boundary for each category based on the structural constraints and uses a distance-based diagnostic algorithm to identify fault diagnosis. We conducted experiments on two publicly available bearing datasets to validate the performance of the model. The results show that the model improves the average accuracy classification by 10.73% and 13.84%, respectively, compared to other comparative model.

Precision autofocus in optical microscopy with liquid lenses controlled by deep reinforcement learning

AbstractMicroscopic imaging is a critical tool in scientific research, biomedical studies, and engineering applications, with an urgent need for system miniaturization and rapid, precision autofocus techniques. However, traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this goal. In response, this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus (DRLAF). The proposed study employs a custom-made liquid lens with a rapid zoom response, which is treated as an “agent.” Raw images are utilized as the “state”, with voltage adjustments representing the “actions.” Deep reinforcement learning is employed to learn the focusing strategy directly from captured images, achieving end-to-end autofocus. In contrast to methodologies that rely exclusively on sharpness assessment as a model’s labels or inputs, our approach involved the development of a targeted reward function, which has proven to markedly enhance the performance in microscope autofocus tasks. We explored various action group design methods and improved the microscope autofocus speed to an average of 3.15 time steps. Additionally, parallel “state” dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples, thereby improving its generalization capability. The experimental results demonstrate that the proposed liquid lens microscope with DRLAF exhibits high robustness, achieving a 79% increase in speed compared to traditional search algorithms, a 97.2% success rate, and enhanced generalization compared to other deep learning methods.

Utilizing isotopic and elemental markers to enhance the authenticity of potatoes

AbstractGiven the economic importance of potato production, establishing the origin of unknown commercial samples declared as local, is particularly important for both producers and competent authorities. In the study presented here, stable isotopic ratios of deuterium, oxygen, carbon and nitrogen, were determined in order to develop a potato provenance methodology. Isotopic determinations were conducted in an alcohol, preceded by the enzymatic degradation of potato starch using α-amylase to yield soluble dextrins and oligosaccharides. Subsequently, the hydrolyzed potato starch underwent fermentation to produce ethanol, which was then obtained through distillation under controlled conditions. The integration of data obtained from analyzing the D/H isotopic ratios using SNIF-NMR spectroscopy in potato samples for the first time, in conjunction with measurements of the 13C/12C, 15N/14N, and 18O/16O ratios via IRMS, yielded a unique isotopic fingerprint for the potato samples under examination. Elemental analysis by ICP-OES also added important information in the dataset. The chemometric analysis by applying OPLS-DA technique, highlighted the parameters of (D/H)II, δ18O, R and Cu as the best discriminator markers. The proposed model gave 94.07% correct classification of the samples, regarding their geographical origin. It is believed that the differentiation of local potatoes is related to the unique geological and climatic conditions existing in the island. All the above-mentioned conclusions are very promising, for the protection of local potato production.

A Comparative Microscopic and Micrometric Analysis of Birds of Different Feather Types for Identification of Species

Feathers are one of the most prominent pieces of evidence left out in a wildlife crime scene, especially in the case of crimes against birds. Very much like hair samples, these are additionally comprised of keratin protein and resist putrefaction and decay. Forensic ornithology manages the assessment and recognizable proof of these feathers through their variable class and individual characters both morphologically and microscopically. Given this, this article aims to analyze the comparative examination of morphological, microscopic, and micrometrical parameters of 13 feather sample types from different endangered birds which may prove useful and crucial in species identification even from degraded feathers obtained at the crime scene involving birds. The analysis of the mean feather length of bird species chosen for this shown variation is related to the type of feather. It has been observed that the feather length of the semiplume feather is significantly higher than for other types of feathers included in this study, that is, wing feathers and contour feathers. Analysis of rachis diameter measurement has shown a significant variation among the chosen bird species with different feather types. Feathers act as trace and corroborative evidence, which can also be used to determine elemental composition to locate the geographical region in the future to set up a reference database for correlation and comparison in case of an unknown sample with the help of this emerging branch of forensic science. Feathers can also act as a source for keratin protein profiling as an alternative tool for species identification.

Extreme R-CNN: Few-Shot Object Detection via Sample Synthesis and Knowledge Distillation.

Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are susceptible to variations within these unknown samples. To mitigate these challenges, we introduce a Two-Stage Fine-Tuning Approach (TFA) named Extreme R-CNN, designed to operate effectively with extremely limited original samples through the integration of sample synthesis and knowledge distillation. Our approach involves synthesizing new training examples via instance clipping and employing various data-augmentation techniques. We enhance the Faster R-CNN architecture by decoupling the regression and classification components of the Region of Interest (RoI), allowing synthetic samples to train the classification head independently of the object-localization process. Comprehensive evaluations on the Microsoft COCO and PASCAL VOC datasets demonstrate significant improvements over baseline methods. Specifically, on the PASCAL VOC dataset, the average precision for novel categories is enhanced by up to 15 percent, while on the more complex Microsoft COCO benchmark it is enhanced by up to 6.1 percent. Remarkably, in the 1-shot scenario, the AP50 of our model exceeds that of the baseline model in the 10-shot setting within the PASCAL VOC dataset, confirming the efficacy of our proposed method.

Application of MATLAB and SAS Viya AI models towards the elucidation of potential microplastics in the Neuse River Basin

Microplastics (MPLs) are ubiquitous particles derived from degradation from plastic refuse or directly from anthropogenic sources. These particles are present in aquatic environments with potential toxicology across the biosphere. In order to characterize their presence, the Neuse River basin was selected for sampling. However, like many MPLs, spectroscopic characterization can be sullied by weathering processes that obscure the native spectra. Therefore, to enhance MPL identification, Principal Component Analysis (PCA), K-means Clustering (KMC), MATLAB, and SAS Viya’s machine learning (ML) modules were implemented on the Neuse River basin's samples. 18 unknown samples were recorded with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy against the controls for PCA and KMC: high-density polyethylene (HDPE), low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and nylon-6 (PA6). Later on, these unknowns were tested against a new set of 900 commercial control samples partitioned by 9 classes for MATLAB and SAS Viya: cellulose acetate (CA), HDPE, LDPE, PP, PS, PVC, PET, polymethyl(methacrylate) (PMMA), and PA6 using µ-FTIR (micro-FTIR) for single-particle discrepancy. Application of MATLAB’s Classification Learner and SAS Viya for Learners software helped aggregate multiple machine learning (ML) models to test for corroboration of predictions with datasets derived from Version 1 (V1), Version 2 (V2), and Version 3 (V3) feature extraction algorithms. Novel feature extractions, based on regressions of discrete data from spectral intensities, in V2 and unique probing of the relationships informing the regressions in V3 showed moderate to moderately high predictor strength, contributing to accuracy increase in V3 according to various predictor strength algorithms in MATLAB. In the test scenario of the strongest version, V3, 63.2% (+ 5.3% from V1) of the models performed very strongly (90% cutoff in accuracy), and 89.5% (+ 0% from V1) of the models performed moderately strongly (80% cutoff in accuracy). The models, coupled with the unsupervised PCA and KMC, indicated microparticle (MP) from Stockinghead Creek in Duplin County, NC; SHR-1b(2), as LDPE. However, the change in corroboration across model types was not significant, according to Kruskal-Wallis (H = 0.555, p = .7577). An ANOVA was performed to see if LDPE incidence was similar across all unknowns, indicating a similar ratio of predictions, excluding NRCP-1 (F = 31.479, p < .0001). While it is unclear if this particle is truly LDPE, the results may suggest that LDPE could be of high presence in the river basin (specifically, PE) when taking into account the predictor set across all versions. Lastly, misclassification was further elucidated for samples “CF-3” and “NRCP-1(2)” which are thought to be extensively-degraded PVC MPLs.

Varying extractability of petrogenic and pyrogenic polycyclic aromatic hydrocarbons (PAH) in urban soils: Evaluation of sample preparation and extraction of 71 PAH and alkylated PAH

PAH extraction methods have been widely studied over the last decades. However, there is still no consistent method for contaminated soils. Here, for the first time, PAH source characteristics in soils were considered for the investigation of different pretreatment methods, extraction techniques and solvents in nine petrogenic to primarily pyrogenic urban soils. Sources were identified by macroscopic identification of source substrates and analysis of 71 PAH and alkylation patterns.The comparison of extraction solvents revealed that a combined sequential extraction using dichloromethane (for petrogenic PAH) and toluene:methanol (6:1) led to the highest extraction efficiencies for all samples and is therefore best suited for PAH analysis of unknown samples. Acetone:n-hexane led to the lowest extraction efficiencies for all samples and is not recommended as extraction solvent. The comparison of extraction techniques showed that Pressurized Solvent Extraction is more efficient than ultrasonic extraction especially for petrogenic PAH, but also for pyrogenic PAH. Grinding increased PAH extractability considerably but mainly for petrogenic PAH from bituminous coal particles. In mixed to primarily pyrogenic samples with a smaller proportion of coal, the enhanced extractability of coal leads to a more petrogenic PAH distribution and consequently a decrease in the source identifying PAH Alkylation Index.It is concluded that PAH contents in unknown (urban) soils, where pyrogenic and petrogenic PAH can be present, cannot generally be compared if different extraction techniques, different extraction solvents and grinding or no grinding are applied. As extraction efficiency increases with more effective methods and solvents for bituminous coals, it is recommended to define a specific extraction technique and solvent mixture for improved comparability in future PAH analyses.

Deployment of intelligent irrigation monitoring system with Android app for machine learning prediction.

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially considering that agriculture accounts for a substantial portion of freshwater usage. The inadequate monitoring resources in agriculture lead to unnecessary wastage of water, affecting crop growth and the water supply. This challenge has spurred us to focus on the agricultural domain, aiming to conserve water by imparting intelligence into irrigation systems by converging IoT and machine learning technologies. Our study has proposed a Smart Irrigation System (SIS), designed to operate remotely, leveraging open-source IoT platforms. Utilizing IoT and machine learning methodologies can effectively optimize water usage in irrigation practices. The designed system comprises hardware and software tools, where comprehensive data is monitored through an application interface. The sensor's interface with a 32-bit microcontroller is used to gather data such as environmental temperature, humidity, and soil moisture and temperature. The user (farmer) continuously receives real-time updates about the condition of the farmland through the Blynk app. The app triggers a notification advising the user to start or stop watering based on pre-set threshold values of soil moisture. Additionally, users can control the water pump remotely through the app. The data collected from the deployed smart irrigation system was used to train a machine learning algorithm incorporating weather parameters and soil temperature to predict soil moisture content. This trained model aims to offer guidance for future assessments of unknown soil samples based on the weather conditions.

Application of improved matrix dilution method in quantitative analysis of Ni-Co-Mn ternary precursor

Abstract When using energy-dispersive X-ray fluorescence (EDXRF) to analyze Ni-Co-Mn (NCM) samples, if the standard sample's concentration greatly differs from the unknown sample's concentration, the traditional matrix dilution method requires repeated dilution and measurement. This makes the process time-consuming and labor-intensive. This study proposes an improved matrix dilution method to reduce sample preparation and analysis. This method first establishes a functional relationship model between the dilution factor and the characteristic X-ray intensity. Then the characteristic X-ray intensity of the analyzed element can be calculated by this model, avoiding unnecessary dilution and measurement steps. To verify the effectiveness of this method, the dilution factors and characteristic X-ray intensities of the test samples were fitted using the established functional relationship. The fitting results showed that the fitting coefficients of determination of the Mn, Co, and Ni were all 0.999. Quantitative analysis was performed on the characteristic X-ray intensity fitting values and measured values of the test samples. The results showed that the quantitative results of the two were consistent. The average error of the three elements for both methods was 1.1% and 0.7%, respectively. It shows that through the established functional relationship, the characteristic X-ray intensity can be effectively calculated by the dilution factor. This method can be applied to samples with identical elements and proportions of target elements, but with different concentrations, using the same set of standard samples.

Design of an Accurate, Planar, Resonant Microwave Sensor for Testing a Wide Range of Liquid Samples

In this paper, an inductively coupled capacitively loaded ring resonator (IC-CLRR)-based microwave resonant sensor has been proposed for the accurate identification of any unknown liquid sample and its permittivity estimation. The key element of this work is the sensor’s wide range capability towards the non-invasive testing of liquids covering a wide dielectric range of liquid samples, i.e., εr = 2 to 80. The proposed microwave sensor is etched over the FR-4 substrate and is excited by the microstrip line through inductive coupling. The placement of an unknown liquid sample in close proximity to the sensor alters its natural resonant frequency due to a change in effective inductance and capacitance as per the dielectric property of the liquid sample. Further, a mathematical formulation using curve fitting has also been derived. The measurement results show a good accuracy in estimating the permittivity and, thus, the unknown liquid identification capability of the designed sensor with a very low error (nearly 5%). This sensor design is simple to fabricate, cost-friendly, and small in size.

Detection of low-level fentanyl concentrations in mixtures of cocaine, MDMA, methamphetamine, and caffeine via surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0-339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm-1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0-0.4 mM, medium = 0.4-14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.

Open-world disaster information identification from multimodal social media

The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. It is worth noting that in real-world scenarios, sudden disaster events may differ from the training data, which may require the multimodal network to predict them as unknown classes instead of misclassifying them to known ones. Previous studies have primarily focused on model accuracy in a closed environment and may not be able to directly detect unknown classes. Thus, we propose a novel multimodal model for categorizing social media related to disasters in an open-world environment. Our methodology entails utilizing pre-trained unimodal models as encoders for each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data to classify known classes, whereas the open-world classifier is used to determine whether the input belongs to a known class. Furthermore, we propose a sample generation strategy that models the distribution of unknown samples using known data, which allows the open-world classifier to identify unknown samples. Our experiments were conducted on two public datasets, namely CrisisMMD and MHII. According to the experimental results, the proposed method outperforms other baselines and approaches in crisis information classification.

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.

Atmospheric Pressure Photoionization with Halogen Anion Attachment for Mass Spectrometric Analysis of Hydrocarbons and Hydrocarbon-Based Polymers.

Aliphatic hydrocarbons and hydrocarbon-based synthetic polymers are of interest in many fields, but their characterization by mass spectrometric methods is generally limited due to their poor ionizability. Recently, atmospheric pressure photoionization (APPI), combined with halogen anion attachment in negative-ion mode, has drawn attention as a potential method for ionizing various polymers without extensive fragmentation or other unwanted side reactions. In this work, the applicability of halogen anion attachment with APPI was studied using several synthetic polymers, including polyethylene, polypropylene, polyisoprene, and polystyrene, as well as simple n-alkanes of various chain lengths. For hydrocarbon-based polymers, the method produced clear distributions of intact polymer adduct ions when different halogen anions were used. It was found that increasing the halogen anion size decreased ionization efficiency, particularly in the absence of π-bonds in the polymer structure. Testing with simple n-alkanes showed that only molecules containing fifty or more carbon atoms formed detectable halogen adducts, possibly due to the low gas-phase stabilities of the lighter n-alkane adduct ions. In conclusion, halogen anion attachment with negative-ion APPI appears to be a highly promising method for polymer analysis, providing structural data and clean polymer mass spectra with minimal fragmentation, which can be useful for the identification of unknown samples.