Low Detection Research Articles

Eco-Friendly Synchronous Spectrofluorimetric Determination of Imipenem, Cilastatin, and Relebactam; Application to Market Formulations and Biological Fluids; Greenness Assessment.

The proposed study introduces a rapid, sensitive, and simple synchronous spectrofluorimetric technique for simultaneous quantification of relebactam, cilastatin, and imipenem in marketed pharmaceutical forms and biological fluids. Using synchronous fluorescence spectroscopy at Δ λ = 110nm, cilastatin was detected at 360nm. Fourier Self-Deconvolution was subsequently applied to the spectrum to estimate relebactam and imipenem at 430nm and 470nm, respectively after detection of cilastatin at 360nm ensuring no cross-interference. The pH was adjusted to 8.0 using 2.0 mL of alkaline borate buffer. This approach allowed for the precise quantification of relebactam, cilastatin, and imipenem through ranges of 50-400 ng mL- 1, 20-500 ng mL- 1, and 50-500 ng mL- 1 respectively. The lower detection and quantitation limits were 9.9 and 29.7 ng mL- 1 for REL, 4.5 and 13.6 ng mL- 1 for CIL and 5.5 and 16.5 ng mL- 1 for IMP. The proposed method was successfully applied for the determination of studied drugs in their pharmaceutical formulations with a high degree of accuracy and without interference from common excipients. This approach allowed for the precise quantification of relebactam, cilastatin, and imipenem through ranges of 50-400 ng mL- 1, 20-500 ng mL- 1, and 50-500 ng mL- 1, respectively. The proposed method was rigorously validated according to ICH guidelines. Furthermore, the method's environmental impact was assessed using Eco-scale and Green Analytical Procedure Index (GAPI) techniques.

Effect of various culinary treatments on the glycoalkaloid content of potato peel

Potatoes are one of the most consumed foods worldwide. During germination, glycoalkaloids (GAs) with potential bioactive properties are produced, which, at high concentrations, may have adverse health effects. Potato peels, a byproduct generated in large quantities but increasingly used in contemporary cuisine, contain significant levels of these GAs. This paper examines GA levels in potato peels after various culinary treatments (boiling, steaming, baking, air frying, conventional deep frying, and double frying) to provide new data and ensure food safety. An optimized extraction method using ultrasound-assisted extraction (UAE) was employed, followed by purification with mesostructured SBA-15 silica functionalized with octadecylsilane (C18) chains as sorbent in solid-phase extraction, prior to HPLC-DAD determination of α-solanine and α-chaconine levels in the samples. The method was validated, obtaining excellent recovery values (88 – 96 %) and low detection and quantification limits (0.3 and 1 mg/L, respectively). Upon application of the validated analytical method to the samples culinarily treated using the most common cooking techniques (boiling, steaming, baking, air frying, conventional deep frying, and double frying) at various times and temperatures, the obtained results demonstrated significant reductions (between 52 % and 84 %) in GAs content in the treated potato peels, compared to fresh samples. Statically significant degradation of these compounds was observed through baking at elevated times and temperatures (74 – 84 %), as well as both conventional deep frying and double frying (76 – 83 %).

Highly Sensitive Electrochemical Detection of Chlorpromazine Using Broccoli‐Like Copper–Aluminum‐Layered Double Hydroxide‐Modified Platinum Electrode

AbstractChlorpromazine (CPZ), an antipsychotic drug derived from phenothiazine, can cause a range of adverse effects in humans, notably visual problems and hematological disorders upon consumption of chlorpromazine‐contaminated water. In this regard, a novel electrochemical sensing platform based on a copper–aluminum‐layered double hydroxide (Cu–Al LDH)‐modified platinum electrode for the highly sensitive and selective detection of chlorpromazine in groundwater samples has been developed. The immobilized Cu–Al LDH showed an electrocatalytic effect on chlorpromazine reduction and oxidation. Among the employed electroanalytical techniques, only the square‐wave voltammetry‐assisted Pt/Cu–Al LDH electrode could detect chlorpromazine with a remarkable sensitivity of 0.065 µA µM−1 over a broad linear detection range of 0.01–760 µM, with low detection and quantification limits of 4.86 and 16.18 nM, respectively. Furthermore, the developed electrode can rapidly detect chlorpromazine within less than 10 s. In addition, the diffusion profiles of steady‐state concentrations of oxidized and reduced chlorpromazine within the immobilized Cu–Al were studied using the Legendre wavelet method. Moreover, the developed Pt/Cu–Al LDH electrode demonstrated satisfactory rates in the quantification of chlorpromazine in groundwater samples, yielding satisfactory recovery rates (97.63–102.57%), confirming the practicability of the fabricated electrode in real‐time monitoring of chlorpromazine levels in groundwater without requiring any sample pretreatment.

Application of magnetic AlFu MOF nanocomposite for the extraction and preconcentration of some pesticides from different distillates

This research used a magnetic AlFu nano-metal-organic framework as an adsorbent for the first time. This approach extracts and preconcentrates eight pesticides from various distillates through a two-step process: magnetic dispersive micro solid phase extraction and dispersive liquid-liquid microextraction. Initially, the nanocomposite is dispersed into a sample solution containing the pesticides and Na2SO4. The target pesticides are then adsorbed onto the nanocomposite, which is subsequently isolated from the aqueous phase using an external magnetic field. Acetonitrile is used to elute the adsorbed analytes pesticides from the nanocomposite surface. The resulting acetonitrile extract, containing the concentrated pesticides, is then mixed with a tiny amount of another solvent and injected into a NaCl solution. Centrifugation allows the organic phase, enriched with the pesticides, to settle down. An aliquot of this organic layer is then analyzed using a gas chromatography-flame ionization detector. Optimization of the procedure led to favorable performance, including good extraction recovery of the pesticides (68–98 %), significant enrichment (enrichment factors of 340–489), a wide range of detectable concentrations (2.90–1400 µg L−1), and low detection (0.15–0.88 µg L−1) and quantification limits. (0.49–2.90 µg L−1)

Deep Learning-Enabled Optimization of Polyp Frame Detection and Segmentation

Polyp segmentation is a method for identifying and characterizing polyps, or abnormal tissue development, as seen in frame images from medical procedures such as endoscopic or colonoscopy frame images. Polyp segments are reliant on high-quality imaging data, which can be intermittently accessible, causing lower detection and precision in segmentation. To overcome these challenges, we propose a novel deep learning (DL) method called intelligent chameleon swarm optimized-adaptive fully convolutional neural network (ICSO-AFConvNet) for polyp segmentation and frame detection. Initially, we gather the dataset named (KVasir-SEG), which contains a thousand frame images, and then contrast limited adaptive histogram equalization (CLAHE) technique, is used to preprocess the data. Experimental findings on the KVasir-SEG dataset reveal that ICSO-AFConvNet outperforms previous approaches in measures such as Dice, Interception over union (IoU), Recall and Precision. These findings show that the suggested ICSO-AFConvNET technique has tough generalization power and learning ability, making it an appealing alternative for practical applications with significant data variances.

Iron saturation and binding capacity of lactoferrin - development and validation of a colorimetric protocol for quality control

Among the numerous biofunctional properties of lactoferrin, its ability to bind iron ions can be considered a core function. The saturation level with ferric iron affects the stability and functionality of the protein. To reliably quantify the iron saturation, an assay based on the color reagent Ferrozine was developed and validated concerning the lower detection (0.023 μg mL−1) and quantification limits (0.069 μg mL−1), as well as precision, recovery and accuracy values.The established assay was used to monitor iron uptake, comparing two commercially available bovine lactoferrin powders. Significant differences between the samples were observed. One sample exhibited nearly ideal binding behavior with a high affinity for ferric iron (saturation > 98 %), while the comparison sample did not exceed saturation values >80 %. This finding underscores the importance of assessing the iron status and binding capacity for the quality evaluation of lactoferrin products.

Enabling Low‐Noise, High‐Detectivity, Stable, and Flexible Perovskite Mesh Nanowire Photodetectors by Phenylethylamine Iodine Doping Strategy

AbstractThe poor stability, high noise, and low detectivity (D*) of perovskite mesh nanowire (PMN) photodetectors (PDs) seriously hinder their practical applications. Here, a phenylethylamine iodine doping strategy (PIDS) is introduced to solve these problems. The PIDS leads to the formation of 2D perovskite PEA2MAx‐1PbxI3x+1 (PEA = phenylethylamine, MA = methylamine) within MAPbI3 PMN, which not only prevents water and oxygen erosion to thwart PMN degradation but also inhibits the transport of dark state carriers to reduce dark current. As a result, the noise, D*, and stability of the PMN PD are simultaneously improved. The device exhibits low noise current (7.61 × 10−15 A Hz−1/2) and high D* of 3.2 × 1014 Jones, the highest D* value for PMN PDs reported to date. Moreover, the unpacked device sustains 100% of its initial performance after 2880 h of storage in the air (45–55% humidity), enabling it as the most stable MAPbI3 perovskite micro/nanostructure PD reported to date. Furthermore, the flexible device with PIDS exhibits comparable performance to that of the rigid device as well as great mechanical stability. Finally, the flexible device with PIDS demonstrates excellent optical imaging capability and a higher precision optical imaging potential than the commercial silicon photodiode S2386.

Blueness, Whiteness, and greenness Appraisal of sensitive chromatographic approach for determination of semaglutide concurrently with Pioglitazone and metformin using green mobile Phase; alongside with pharmacokinetic study

The replacement of hazardous solvents with greener alternatives was under consideration to achieve a sustainable and green approach. The assessment of reliable white analytical method for the determination of semaglutide as a novel member in glucagon-like peptide agonists class is still of a great importance. That is because pharmacological, clinical, and therapeutic drug monitoring purposes. This research proposes and fully validates a simple and sensitive RP-HPLC method for the determination of semaglutide simultaneously with pioglitazone and metformin. The suggested procedures utilizing a green mobile phase of 8 % glycerol in 0.01 M phosphate buffer (pH 6). Separation was achieved using a cyano (5.0 μm, 150 × 4.6 mm) column maintained at a temperature of 40 °C. The mixture was isocratically eluted under a flow rate of 1 mL/min with a total run time of less than 8 min. The UV-detector was adjusted at 215 nm. The method provides low detection (ranging from 0.003 to 0.63 µg/mL) and quantification (ranging from 0.008 to 1.9 µg/mL) limits, with good precision (%RSD ranging from 0.66 % to 0.89 %). The suggested technique has been effectively used to determine each drug in its pharmaceutical preparation and spiked human plasma. Beside the remarkable sensitivity and sustainability of the evolved approach, the relevance of therapeutic drug monitoring motivated us to perform a pharmacokinetics study for both semaglutide and metformin in real human plasma samples. Greenness, whiteness and blueness assessment of the developed method have been carried out using different tools (Green Analytical Procedure Index (GAPI), the analytical GREEnness metric approach (AGREE), RGB-12 model and BAGI). The proven green and white profile of the evolved method could open future prospective for the clinical research and regular quality control examination of the investigated mixture with the least negative environmental impacts.

Highly selective triethylamine gas sensor based on ZnO/Ti3C2Tx MXene self-assembly heterostructure

MXenes, as new two-dimensional transition metal compounds, has become promising sensing materials due to the excellent metal conductivity, abundant adsorption sites and the terminal groups of -O, -OH and -F. In this study, we report the well-etched accordion-like Ti3C2Tx by removing Al layer in Ti3AlC2, where Tx represents a wealth of terminal functional groups (-OH, -F, -O, etc.). In addition, unique hierarchical self-assembly ZnO/Ti3C2Tx nanocomposites are synthesized by hydrothermal method. The Brunauer-Emmett-Teller Method investigation also shows that the ZnO/Ti3C2Tx nanocomposites has a specific surface area of about 22.08 m2/g, and the pore size is mainly located at 11.1 nm. The innovative nanostructure is conducive to the adsorption and response of gas molecules. By investigating the gas sensitive properties of the sensors, it is found that the response of the ZnO/Ti3C2Tx sensor to triethylamine (TEA) is improved by 66 times and 1.58 times over that of pure Ti3C2Tx and ZnO, and the optimal operating temperature is 60℃ lower than that of pure ZnO. In addition, the LOD (Low Of Detection) of about 32.7 ppb indicates that the ZnO/Ti3C2Tx sensor is suitable for the detection of low concentrations TEA. The sensing performances of the ZnO/Ti3C2Tx sensor at a high humidity of 75 % RH verifies the strong sensing performance in high humidity environment. Furthermore, the composite sensor stabilize in 6 repetitive tests and present slight decrease in response value over 30 days. The excellent TEA sensitive properties indicate that the ZnO/Ti3C2Tx nanocomposite sensor may have promising applications in the gas sensitive field.

Camouflage detection: Optimization-based computer vision for Alligator sinensis with low detectability in complex wild environments

Alligator sinensis is an extremely rare species that possesses excellent camouflage, allowing it to fit perfectly into its natural environment. The use of camouflage makes detection difficult for both humans and automated systems, highlighting the importance of modern technologies for animal monitoring. To address this issue, we present YOLO v8-SIM, an innovative detection technique specifically developed to significantly enhance the identification precision. YOLO v8-SIM utilizes a sophisticated dual-layer attention mechanism, an optimized loss function called inner intersection-over-union (IoU), and a technique called slim-neck cross-layer hopping. The results of our study demonstrate that the model achieves an accuracy rate of 91 %, a recall rate of 89.9 %, and a mean average precision (mAP) of 92.3 % and an IoU threshold of 0.5. In addition, the model operates at a frame rate of 72.21 frames per second (FPS) and excels at accurately recognizing objects that are partially visible or smaller in size. To further improve our initiatives, we suggest creating an open-source collection of data that showcases A. sinensis in its native environment while using camouflage techniques. These developments collectively enhance the ability to detect disguised animals, thereby promoting the monitoring and protection of biodiversity, and supporting ecosystem sustainability.

Multisource information fusion method for vegetable disease detection

Automated detection and identification of vegetable diseases can enhance vegetable quality and increase profits. Images of greenhouse-grown vegetable diseases often feature complex backgrounds, a diverse array of diseases, and subtle symptomatic differences. Previous studies have grappled with accurately pinpointing lesion positions and quantifying infection degrees, resulting in overall low recognition rates. To tackle the challenges posed by insufficient validation datasets and low detection and recognition rates, this study capitalizes on the geographical advantage of Shouguang, renowned as the “Vegetable Town,” to establish a self-built vegetable base for data collection and validation experiments. Concentrating on a broad spectrum of fruit and vegetable crops afflicted with various diseases, we conducted on-site collection of greenhouse disease images, compiled a large-scale dataset, and introduced the Space-Time Fusion Attention Network (STFAN). STFAN integrates multi-source information on vegetable disease occurrences, bolstering the model’s resilience. Additionally, we proposed the Multilayer Encoder-Decoder Feature Fusion Network (MEDFFN) to counteract feature disappearance in deep convolutional blocks, complemented by the Boundary Structure Loss function to guide the model in acquiring more detailed and accurate boundary information. By devising a detection and recognition model that extracts high-resolution feature representations from multiple sources, precise disease detection and identification were achieved. This study offers technical backing for the holistic prevention and control of vegetable diseases, thereby advancing smart agriculture. Results indicate that, on our self-built VDGE dataset, compared to YOLOv7-tiny, YOLOv8n, and YOLOv9, the proposed model (Multisource Information Fusion Method for Vegetable Disease Detection, MIFV) has improved mAP by 3.43%, 3.02%, and 2.15%, respectively, showcasing significant performance advantages. The MIFV model parameters stand at 39.07 M, with a computational complexity of 108.92 GFLOPS, highlighting outstanding real-time performance and detection accuracy compared to mainstream algorithms. This research suggests that the proposed MIFV model can swiftly and accurately detect and identify vegetable diseases in greenhouse environments at a reduced cost.

Data Independent Acquisition Mass Spectrometry Enhanced Personalized Glycosylation Profiling of Haptoglobin in Hepatocellular Carcinoma.

Aberrant glycosylation has gained significant interest for biomarker discovery. However, low detectability, complex glycan structures, and heterogeneity present challenges in glycoprotein assay development. Using haptoglobin (Hp) as a model, we developed an integrated platform combining functionalized magnetic nanoparticles and zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) for highly specific glycopeptide enrichment, followed by a data-independent acquisition (DIA) strategy to establish a deep cancer-specific Hp-glycosylation profile in hepatitis B virus (HBV, n = 5) and hepatocellular carcinoma (HCC, n = 5) patients. The DIA strategy established one of the deepest Hp-glycosylation landscapes (1029 glycopeptides, 130 glycans) across serum samples, including 54 glycopeptides exclusively detected in HCC patients. Additionally, single-shot DIA searches against a DIA-based spectral library outperformed the DDA approach by 2-3-fold glycopeptide coverage across patients. Among the four N-glycan sites on Hp (N-184, N-207, N-211, N-241), the total glycan type distribution revealed significantly enhanced detection of combined fucosylated-sialylated glycans, which were the most dominant glycoforms identified in HCC patients. Quantitation analysis revealed 48 glycopeptides significantly enriched in HCC (p < 0.05), including a hybrid monosialylated triantennary glycopeptide on the N-184 site with nearly none-to-all elevation to differentiate HCC from the HBV group (HCC/HBV ratio: 2462 ± 766, p < 0.05). In summary, DIA-MS presents an unbiased and comprehensive alternative for targeted glycoproteomics to guide discovery and validation of glyco-biomarkers.

Staggered band alignment of n-Er2O3/p-Si heterostructure for the fabrication of a high-performance broadband photodetector

The low responsivity of conventional Silicon photodiodes in ultraviolet and near-infrared regimes restricts their utility as broadband photodetectors (BBPDs). Despite ongoing investigations into various p-n heterostructures for Silicon-based BBPDs, challenges such as high dark current (Idark), low collection efficiency, low detectivity, and compatibility issues with large-scale Silicon-based devices persist. In this context, we have fabricated relatively unexplored n-Er2O3/p-Si heterojunction-based BBPDs. Polycrystalline Er2O3 thin films (∼110 nm) were deposited on p-Si 〈100〉 substrates by radio frequency magnetron sputtering. Although this process induces a microstrain of approximately 0.022 and a dislocation density of about 0.00303/nm2, the presence of optically active defects is minimal, indicated by a low Urbach energy (∼0.35 eV). X-ray photoelectron spectroscopy (XPS) analysis confirms staggered band alignment at the heterointerface, facilitating efficient charge carrier separation and transport. Consequently, the In/p-Si/n-Er2O3/In device demonstrated significant BBPD properties– low Idark ∼0.15 μA (at +5 V), photo-to-dark current ratio (PDCR) ∼6.5 (at +5 V, 700 nm) with a maximum photoresponsivity ∼22.3 A W−1, and impressive detectivity (∼1013 Jones) even in UV-C region where traditional silicon-based photodetectors respond feebly. The device also demonstrates transient photo-response across an ultrawide spectrum (254 nm–1200 nm) with a fast rise time/fall time ∼79 ms/∼86 ms (at −5 V for 600 nm illumination). This work establishes a straightforward and reliable method for proper material engineering, surface texturing, staggered heterojunction formation, and high-performance BBPD fabrication with prominent broad-spectrum responsivity, sizeable detectivity, and fast response. The integration of these BBPDs with Silicon opens possibilities for their use in electronic devices containing optical switches for communications and broadband image sensors, enhancing their utility in various applications.

Spread spectrum for covert communication in ultra-violet communication system.

In this paper, we propose a covert communication scheme based on a spread spectrum for ultra-violet communication. We first characterize the system model of the communication system, where Warden is adopted to monitor the legitimated user transmission based on the binary hypothesis test and Kolmogorov-Smirnov test. To avoid periodic transmission of the pilot sequence which potentially degrades covertness, we propose a blind synchronization approach, such that the pilot sequence does not need to be transmitted. It is observed from system-level simulation that the spread spectrum-based approach can achieve both low detection bit error rate and covertness under weak light intensity. Moreover, we experimentally evaluate the detection performance and covertness of the proposed approach.

Influence of geometric parameters on aerodynamic characteristics of serpentine convergent-divergent nozzle

The serpentine convergent-divergent nozzle represents an optimal configuration for next-generation fighter aircraft characterized by low detectability and high thrust-to-weight ratio. In contrast to the serpentine convergent nozzle, such configuration offers increased design flexibility with additional parameters, leading to heightened interactions among these parameters. As such, it is crucial to reveal the influence of design parameters on the aerodynamic performance of the serpentine convergent-divergent nozzle and the multifactor interaction, as well as its mechanism. Therefore, the influence, interaction and sensitivity of parameters on the aerodynamic performance of the nozzle were numerically investigated using the orthogonal test method. Additionally, the influence mechanism of the convergence angle, throat aspect ratio, and axial length to inlet diameter on the flow characteristics of the nozzle was investigated in detail. The results show that the convergence angle is identified as the main factor affecting the aerodynamic parameters of the nozzle. As the convergence angle increases, the thrust coefficient, total pressure recovery coefficient and discharge coefficient gradually decrease. The interaction between throat aspect ratio and other parameters is obvious. Different design parameters affect the local loss and the friction loss by affecting the curvature and wetted perimeter area, resulting in different aerodynamic characteristics of serpentine convergent-divergent nozzle.

A hydrologic and land cover‐based habitat model for use in bog turtle (Glyptemys muhlenbergii) conservation

Abstract The endangered bog turtle (Glyptemys muhlenbergii) exemplifies issues related to rare species conservation; presence surveys have low detection and variables used in habitat models can lack relevance to established biological relationships of the species to its environment. The species' use of groundwater saturated soils and stream networks as core habitat and dispersal corridors has been documented. Less is known about the landscape factors that promote the formation and persistence of wetlands used for core habitat. A GIS‐based resource selection function was developed to predict bog turtle habitat use. Trained on 1 ha plots centred on occupied sites and pseudo‐absent plots constrained to areas within 56.4 m to stream network centrelines, the model tested the capacity of a topographic wetness index (TWI), stream order and soil, wetland and land cover type to predict the presence of suitable habitat and turtle occupancy. Landscape variables were sampled at 10 m resolution, but variable selection and model performance were analysed at 100 m resolution to maintain a biologically relevant 1 ha habitat scale and accommodate the resolution of other variables. Suitable habitat and turtle presence were best predicted by intermediate to high values of TWI, land cover with low vegetation height and wetlands, second‐ and third‐order streams and the occurrence of mapped National Wetland Inventory polygons and hydric soils. Very high values of TWI were negatively associated with habitat suitability. The area under the curve of the best model was 0.833. Suitable habitat was found on 88% of 55 independent sites selected using the model and nine new occupied sites were confirmed. Model error is discussed with consideration of human‐altered drainage networks on the agricultural landscape.

Symbiotic Electromagnetic Shadow for Regional Invisibility and Camouflage.

Low detectability and camouflage skills in the electromagnetic wave and light frequency range provide survival advantages for natural creatures and are essential for understanding the operational principles of the biosphere. Taking inspiration from natural mutualistic symbiosis, this paper proposes a symbiotic electromagnetic shadow camouflage mechanism based on a superdispersive surface, aiming to investigate its impact on the observability of specific objects. The design and experimental results indicate that the symbiotic shadow dihedral can significantly reduce overall scattering quantity, which reaches at least 10 dB shrink in the 12-18 GHz frequency range compared to the contrast object. Unlike known camouflage methods, the electromagnetic shadow technology shrinks the overall scattering without any coating and shield metal target while probably offering extensive functional design freedom for the concealed object, creature, or equipment. This also provides a hint to explore symbiosis-related camouflage phenomena in nature.

Construction of 3D spinel binary metal oxide flower decorated 1D halloysite nanotube electrocatalyst for the selective detection of arsenic drug pollutant roxarsone

Heavy metals, including arsenic (As), are categorized as a Group I toxic carcinogen and pose significant threats to the environment and habitats. Roxarsone (RXN) is an organoarsenic antibiotic drug commonly employed as a veterinary curative agent and growth promoter for poultry. Due to its widespread use, it substantially contributes to the accumulation of As in humans and ecosystems. Therefore, it is essential to monitor this potential drug in various real-world samples with high accuracy. Consequently, we have designed 3D zinc cobalt oxide (ZCO) with a 1D halloysite nanotube (HNT) based electrochemical sensor. The functional and morphological features of the electrocatalysts were evaluated with numerous analytic methods. The electrocatalytic property was investigated with different voltammetric measurements. Based on the outcomes, our ZCO/HNT-modified sensor displayed good linear ranges (0.01 to 127 μM and 127 μM to 1147 μM), low detection (1.6 nM), and quantification (5.2 nM) limits, optimal sensitivity (0.465 μA μM−1 cm−2), good selectivity, remarkable performance, and good stability results. Further, this sensor was applied to monitor RXN in various real-world samples and consequently, obtained the well-resolved results. Hence, our ZCO/HNT is an efficacious electrocatalyst for the precise detection of RXN.

Automated online solid-phase extraction-tandem mass spectrometry detection for simultaneous analysis of acidic and alkaline catecholamines and their metabolites in human urine

Metabolic dysregulation of catecholamines (CAs) is implicated in various human diseases. Simultaneously analyzing these acidic and alkaline CAs and their metabolites poses a significant challenge for clinical detection. This study introduces an efficient method employing automated online solid-phase extraction coupled with tandem mass spectrometry (aoSPE-MS/MS). The method employs weak cation exchange (WCX) and mixed-mode anion exchange (MAX) adsorbents to fabricate an on-line solid-phase extraction (SPE) column, along with an automated injection and multi-valve switching capabilities. The setup allows for automated extraction and analysis of urine samples in 15 minutes while retaining a wide range of acidic and basic CAs and their metabolites. The applicability of this method was demonstrated by optimising the adsorbent dosage volume, extraction solvent, and extraction rate. The limits of detection (LODs) and limits of quantitation (LOQs) for the 8 CAs and their metabolites were determined using the aoSPE-MS/MS approach, with ranges of 0.0625 ∼ 62.5 ng/mL and 0.125 ∼ 125 ng/mL, respectively. Additionally, assessments were made on the linearity, accuracy, and precision within and between batches, as well as matrix and ionic effects, and spiked recoveries. The study discovered that the aoSPE-MS/MS technique simplifies operation, increases efficiency, saves time, and has low detection and quantification limits when detecting a wide range of acid and alkaline CAs and their metabolites in urine. The study successfully demonstrated the high-throughput and automated detection of the 8 CAs and their metabolites with varying acidity and alkalinity in human urine samples. This method is expected to be a potential powerful tool for clinical detection.

Electrochemical sensing of cortisol by gold nanoparticle incorporated carboxylated graphene oxide based molecularly imprinted polymer

In this work, we report a simple electrochemical biosensor for cortisol (Cor) detection using a molecularly imprinted polymer (MIP). MIP was prepared by graft copolymerization method on allylated gold nanoparticle (Au) incorporated carboxylated graphene oxide (allylated Au/GO-COOH) polymerized along with the template molecule Cor. Cor is one of the important corticosteroid hormones in human physiology. Cor, the stress hormone, is a steroid hormone that comes under the class of glucocorticoid hormones and is produced as a part of our body’s stress response. This Cor imprinted polymer (Cor-MIP) was chosen to modify the working glassy carbon electrode surface. Thereby developing a highly selective and sensitive electrochemical biosensor. Electrochemical Impedance Spectroscopy and Cyclic Voltammetry explored the electrochemical properties of the developed electrochemical biosensor. The investigation reports from the electrochemical studies revealed that the current value increases proportionally with increasing concentration of Cor. This confirmed the excellent electrocatalytic activity of the prepared Cor-MIP based biosensor toward Cor. The nanomaterials as well as the electroactive sites on Cor-MIP together enhancing the electron transfer rate and lower detection. Differential Pulse Voltammetry is used to find the limit of detection and quantification, and were obtained as, 0.61 × 10−14 M and 2.02 × 10−14 M, respectively. With excellent specificity, stability, and selectivity, this newly developed electrochemical biosensor has been successfully used for Cor measurements in human blood serum samples.