Analytical Signal Research Articles

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were constructed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which characterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.

Improving the accuracy of a digital spectral correlation-interferometric method of direction finding with analytical signal reconstruction for processing an incomplete spectrum of the signal

A method of correlation-interferometric direction finding has been improved, which effectively solves the problem of radio direction finding of radio emission sources under conditions of exposure to one or two masking interference. The problem was solved using the selection of an unmasked fragment of the spatial spectrum of the signal and the reconstruction of the missing samples of its signal group. As a result of the synthesis of the proposed method, estimates of signal samples were obtained as exact solutions to the proposed energy balance equations. The resulting solutions provide a significant increase in the signal-to-interference ratio and, accordingly, direction-finding accuracy without increasing the number of reception channels of the antenna array. As a result of the simulation, the dependences of the standard deviation of the bearing estimate on the signal-to-noise ratio in the presence of interference were built. Under the influence of one or two masking interferences and a signal-to-interference ratio of 0 dB, the use of the known direction-finding method without interference selection produces an anomalously large direction-finding error of more than 0.42 degrees, which is practically independent of the signal-to-noise ratio. The direction-finding method with selection of spectral signal samples masked by interference reduces the direction-finding error to 0.22 degrees when exposed to one interference and to 0.3 degrees when exposed to two interferences. This is due to the presence of power losses of the usable signal during the selection of its samples masked by interference. The proposed method of direction finding with reconstruction of signal samples provides a significant gain in accuracy by 3–30 times compared to the method of selection of masked samples in the range of changes in the signal-to-noise ratio (–20.5) dB. The direction-finding error of the proposed method decreases with increasing signal/noise according to a hyperbolic dependence. It is advisable to use the proposed direction-finding method when masking no more than two samples of the signal group

Contribution of Aeromagnetic Data to the Structural Discontinuities Identification of Black Volta Catchment Aquifer System in Côte d'Ivoire

This paper focuses on improving the structural knowledge of the fissured aquifers of the Black Volta catchment in Côte d'Ivoire based on the mapping of magnetic lineaments, which represent magnetic discontinuities such as magmatic body contacts or tectonic faults. Four filtering methods, pole reduction (equator), gradient (vertical and hori-zontal), upward extension, and angle tilt, were applied to the residual magnetic field map to extract magnetic discontinuities while using the Oasis Montaj (Geosoft) program. Euler deconvolution coupled with the analytical signal provided, in addition to the horizontal location of the magnetic contacts, is an indication of their depths. The resulting structural map contains 458 structures, with lengths ranging from 9.03 to 66.54 km. Three directions, NW-SE, E-W and NE-SW, were detected with a predominance of the NW-SE direction. Depths estimated by Euler solutions range from 6.8 to 2847 m. This map is consistent with many faults already recognised or assumed by traditional structural studies and tectonic events affecting the Ivorian basement. These results contribute significantly to the improvement of the structural map of the Black Volta Basin in Côte d'Ivoire. In addition to the major known tectonic faults, numerous lineaments, particularly those at depth, have been highlighted by the present study.

Matrix Effect Evaluation in GC/MS-MS Analysis of Multiple Pesticide Residues in Selected Food Matrices

Multi-analyte methods based on QuEChERS sample preparation and chromatography/mass spectrometry determination are indispensable in monitoring pesticide residues in the feed and food chain. QuEChERS method, even though perceived as convenient and generic, can contribute to sample matrix constituents’ introduction to the measuring system and possibly affect analytical results. In this study, matrix effects (ME) were investigated in four food matrices of plant origin (apples, grapes, spelt kernels, and sunflower seeds) during GC-MS/MS analysis of >200 pesticide residues using QuEChERS sample preparation. Data analysis revealed considerable analyte signal enhancement and suppression: strong enhancement was observed for the majority of analytes in two matrices within the commodity groups with high water content—apples, and high acid and water content—grapes (73.9% MES and 72.5% MEA, and 77.7% MES and 74.9% MEA, respectively), while strong suppression was observed for matrices within the commodity groups with high starch/protein content and low water and fat content—spelt kernels, and high oil content and very low water content—sunflower seeds (82.1% MES and 82.6% MEA, and 65.2% MES and 70.0% MEA, respectively). Although strong matrix effects were the most common for all investigated matrices, the use of matrix-matched calibration for each sample type enabled satisfactory method performance, i.e., recoveries for the majority of analytes (up to roughly 90%, depending on the fortification level and matrix type), which was also externally confirmed through participation in proficiency testing schemes for relevant food commodity groups with the achieved z-scores within acceptable range ≤ |2|.

Study on simultaneous determination of OCPs, PCBs and PBDEs in fish sample: application for marine fish tissues collected from Hai Phong province

In this work, a GC-MS/MS-based analytical method using accelerated solvent extraction combined with multi-layer silica gel column for sample preparation was developed to simultaneously determine 20 organochlorine pesticides (OCPs), 28 polychlorinated biphenyls (PCBs) and 8 polybrominated diphenyl ethers (PBDEs) in fish tissue. The method detection limits (MDLs) were achieved in the range of 0.053 ng/g (α-chlordane) – 1.65 ng/g (δ-BHC), 0.07 ng/g (PCB-209) – 1.84 ng/g (PCB-28), 0.323 ng/g (BDE-209) – 0.796 ng/g (BDE-47) for OCPs, PCBs and PBDEs, respectively. Intra-day and inter-day repeatability of the analytical signal (peak area) were below 10.5% and 12.4%, correspondingly. The overall recovery was investigated by spiking experiments and ranged from 70.9 to 114%. The confirmation of this developed method was assessed by analysis of the standard reference material (SRM-1947) sample. The measured concentrations of target compounds were within the range of the certified values. This developed method was applied for analysis of OCPs, PCBs and PBDEs in five fish tissues collected randomly from local markets at Hai Phong province. Their concentrations (<MDL – 206 ng/g for OCPs, <MDL – 20.7 ng/g for PCBs and <MDL – 66.7 ng/g for PBDEs) were lower than the maximum residue levels permitted by European Union, Food and Agriculture Organization and World Health Organization.

Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning

Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians in diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form diagnostic B-mode images. However, the components of breast tissue, such as glandular tissue, fat, and lesions, differ in sound speed. Given a constant sound speed assumption, these differences can degrade the quality of reconstructed images via phase aberration. Sound speed images can be a powerful tool for improving image quality and identifying diseases if properly estimated. To this end, we propose a supervised deep-learning approach for sound speed estimation from analytic ultrasound signals. We develop a large-scale simulated ultrasound dataset that generates representative breast tissue samples by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We adopt a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map. The simulated tissue is interrogated with a plane wave transmit sequence, and the complex-value reconstructed images are used as input for the convolutional network. The network is trained on the sound speed distribution map of the simulated data, and the trained model can estimate sound speed given reconstructed pulse-echo signals. We further incorporate thermal noise augmentation during training to enhance model robustness to artifacts found in real ultrasound data. To highlight the ability of our model to provide accurate sound speed estimations, we evaluate it on simulated, phantom, and in-vivo breast ultrasound data.

Matrix effects demystified: Strategies for resolving challenges in analytical separations of complex samples.

Matrix effects can significantly impede the accuracy, sensitivity, and reliability of separation techniques presenting a formidable challenge to the analytical process. It is crucial to address matrix effects to achieve accurate and precise measurements in complex matrices. The multifaceted nature of matrix effects which can be influenced by factors such as target analyte, sample preparation protocol, composition, and choice of instrument necessitates a pragmatic approach when analyzing complex matrices. This review aims to highlight common challenges associated with matrix effects throughout the entire analytical process with emphasis on gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, and sample preparation techniques. These techniques are susceptible to matrix effects that could lead to ion suppression/enhancement or impact the analyte signal at various stages of the analytical workflow. The assessment, quantification, and mitigation of matrix effects are necessary in developing any analytical method. Strategies can be implemented to reduce or eliminate the matrix effect by changing the type of ionization, improving extraction and clean-up methods, optimization of chromatography conditions, and corrective calibration methods. While development of an effective strategy to completely mitigate matrix effects remains elusive, an integrated approach that combines sample preparation, analytical extraction, and effective instrumental analysis remains the most promising avenue for identifying and resolving matrix effects.

HOMO- AND HETERONUCLEAR Yb(III) AND Lu(III) COMPLEXES WITH CALIX[4]ARENE MODIFIED WITH PORPHYRIN SUBSTITUENTS.

In this work we describe the approaches for the synthesis of lanthanide-containing homo- and heteronuclear complexes using 5,11,17,23-tetra-tert-butyl-25,27-dihydroxy-26,28-bis[(N-meso-­(p-aminophenyl)-meso-triphenylporphyrincarbonyl)methoxy]-calix[4]arene (1). Such spatially preorganizedcalix[4]arene macrocycle facilitates lanthanide cation coordination, impacting the structure and analytical signal of porphyrin substituents. The study employs two synthesis stra­tegies resulting in mononuclear and homobinuclear complexes, influenced by steric effects and changes in hydrogen bonding. The complexes exhibit changes in fluorescence spectra due to intramolecular dimerization and hydrogen bonding during complex formation. Mononuclear complexes (Ln-1) are synthesized under mild conditions, while homobinuclear complexes (Ln2-1) require high-boiling solvents and specific reaction conditions, detailed synthesis procedures are described. The structural changes during complex formation were analyzed with the use of NMR analysis. The absorption spectra of lanthanide complexes demonstrate shifts in maxima, reflecting coordination changes, with notable variations between mononuclear and homobinuclear complexes. Additionally, heteronuclear compounds with copper(II), zinc(II), and palladium(II) exhibit distinctive absorption patterns, emphasizing the role of d-metal ions. Luminescence stu­dies reveal the sensitization of 4f-luminescence by both calix[4]arene and porphyrin fragments. The use of deuterated solvents enhances 4f-luminescence intensity, highlighting solvent effects. Furthermore, 4f-luminescence characteristics were investigated in both solid and solution states for heterometallic compounds, emphasizing differences between copper-containing and zinc/palladium-containing complexes. The results offer valuable insights into the design, synthesis, and spectral properties of these complexes, showcasing their potential applications in various fields, and the findings contribute to the understanding of lanthanide-porphyrin systems and their coordination behavior.

Activated Screen-Printed Boron-Doped Diamond Electrode for Rapid and Highly Sensitive Determination of Curcumin in Food Products.

Due to a great interest in the beneficial properties of polyphenolic antioxidant curcumin (CCM), sensitive and accurate methods for determining CCM are needed. The purpose of our research was to develop a very simple, fast, and sensitive differential pulse adsorptive stripping voltammetric (DPAdSV) procedure using an electrochemically activated screen-printed boron-doped diamond electrode (aSPBDDE) for the determination of CCM. The activation of the SPBDDE was accomplished in a solution of 0.1 mol/L NaOH by performing five cyclic voltammetric scans in the range of 0-2 V, at ν of 100 mV/s. The changes in surface morphology and the reduction of the charge transfer resistance due to the activation of the electrode resulted in the amplification of the CCM analytical signal on the aSPBDDE. As a result, an extremely sensitive measurement tool was formed, which under optimized conditions (0.025 mol/L PBS of pH = 2.6, Eacc of 0.3 V, tacc of 90 s, ΔEA of 100 mV, ν of 150 mV/s, and tm of 10 ms) allowed us to obtain a limit of detection (LOD) of 5.0 × 10-13 mol/L. The aSPBDDE has proven to be a highly effective tool for the direct determination of CCM in food samples with high accuracy and precision. The results are in agreement with those obtained using ultra-high-performance liquid chromatography coupled with mass spectrometry and electrospray ionization (UHPLC-ESI/MS).

Electrochemical vs. Optical Biosensors for Point-of-Care Applications: A Critical Review

Analytical chemistry applied to medical and diagnostic analysis has recently focused on the development of cost-effective biosensors able to monitor the health status or to assess the level of specific biomarkers that can be indicative of several diseases. The improvement of technologies relating to the possibility of the non-invasive sampling of biological fluids, as well as sensors for the detection of analytical signals and the computational capabilities of the systems routinely employed in everyday life (e.g., smartphones, computers, etc.), makes the complete integration of self-standing analytical devices more accessible. This review aims to discuss the biosensors that have been proposed in the last five years focusing on two principal detecting approaches, optical and electrochemical, which have been employed for quantifying different kinds of target analytes reaching detection limits below the clinical sample levels required. These detection principles applied to point-of-care (POC) devices have been extensively reported in literature, and even the limited examples found on the market are based on these strategies. This work will show the latest innovations considering the integration of optical and electrochemical detection with the most commonly reported analytical platforms for POC applications such as paper-based or wearable and implantable devices.

Geobody estimation by Bhattacharyya method utilizing nonlinear inverse modeling of magnetic data in Baba-Ali iron deposit, NW Iran

One of the essential geophysical concerns is the estimation of the physical and geometrical parameters of the reserve (geobody), which is done by exploiting the nonlinear inverse modeling of magnetic data. The present study includes preparing and modeling magnetic data to suggest the Baba Ali Iron ore deposit's drilling locations in NW Iran. The area is covered with 1000 points of geomagnetic reading with an almost 5 × 10 m2 regularly spaces grid trending WE. The areal and depth extent of the iron ore geobody was unknown. The Bhattacharyya method by MATLAB software coding was employed to underestimate the misfit function and re-construct potential field data, providing the most suitable fitting with measured magnetic data. In this order, the residual calculated anomaly exhibited an excellent two-dimensional conformation with forward modeling. Furthermore, 3D modeling correctly reconstructs the anomalies' productive resources' properties. After preparing full magnetic maps, the magnetic lenses distinguished in four anomalies of surface depths, 20, 50, and deeper than 50 m for this zone. This magnetite lens for the first zone was estimated based on analytical signal filters applied on the entire magnetic map so that the lens's depth is trivial and almost zero. Due to specific gravity calculated as 4.77 t/m3, initial storage capacity is suggested to be about 95,400 tons of magnetite, pyrite, and hematite minerals at most in an area about 6 Km2. Finally, to complete the preliminary explorations of the specified area, exploratory drilling is suggested for three points by inverse modeling. Regarding this study as the first try in magnetic reconnaissance step of Iron mineral exploration in the study area, there is no geological constraints available based on drilling evidences. However, the model is well satisfies the surface anomalies considering residual magnetic property.

The Development of Reagentless Amperometric Glucose Biosensor Based on Gold Nanostructures, Prussian Blue and Glucose Oxidase.

Precise blood glucose detection plays a crucial role in diagnosing and medicating diabetes, in addition to aiding diabetic patients in effectively managing their condition. In this research, a first-generation reagentless amperometric glucose biosensor was developed by combining the graphite rod (GR) electrode modification by gold nanostructures (AuNS) and Prussian blue (PB) with glucose oxidase (GOx)-an enzyme that can oxidize glucose and produce H2O2. Firstly, AuNS was electrochemically deposited on the GR electrode (AuNS/GR), and then PB was electrochemically synthesized on the AuNS/GR electrode (PB/AuNS/GR). Finally, GOx was immobilized over the PB/AuNS nanocomposite with the assistance of Nafion (Nf) (Nf-GOx/PB/AuNS/GR). An application of PB in the design of a glucose biosensor enables an easy electrochemical reduction and, thus, the determination of the H2O2 produced during the GOx-catalyzed oxidation of glucose in the sample at a low operation potential of -0.05 V vs. Ag/AgCl/KCl3 mol L-1. In addition, AuNS increased the electrochemically active surface area, improved the GOx immobilization and ensured a higher analytical signal. The developed glucose biosensor based on the Nf-GOx/PB/AuNS/GR electrode exhibited a wide linear range, from 0.025 to 1 mmol L-1 of glucose, with a 0.0088 mmol L-1 limit of detection, good repeatability and high selectivity over electroactive interfering substances. The developed biosensor is convenient for the determination of glucose in the physiological environment.

Analytical Validation of Two Assays for Equine Ceruloplasmin Ferroxidase Activity Assessment.

Ceruloplasmin (Cp) assessment in biological samples exploits the oxidase activity of this enzyme against several substrates, such as p-phenylenediamine (p-P), o-dianisidine (o-D) and, most recently, ammonium iron(II) sulfate (AIS). Once developed in humans, these assays are often used in veterinary medicine without appropriately optimizing in the animal species of interest. In this study, two assays using AIS and o-D as substrates have been compared and validated for Cp oxidase activity assessment in horse's plasma. The optimization of the assays was performed mainly by varying the buffer pH as well as the buffer and the substrate molar concentration. Under the best analytical conditions obtained, the horse blood serum samples were treated with sodium azide, a potent Cp inhibitor. In the o-D assay, 500 µM sodium azide treatment completely inhibits the enzymatic activity of Cp, whereas, using the AIS assay, a residual analytical signal was still present even at the highest (2000 µM) sodium azide concentration. Even though the analytical values obtained from these methods are well correlated, the enzymatic activity values significantly differ when expressed in Units L-1. A disagreement between these assays has also been detected with the Bland-Altman plot, showing a progressive discrepancy between methods with increasing analytical values.

Diagnostic development using net analyte signal for pure component modeling approaches

The use of calibration-free or minimal calibration chemometric modeling approaches has garnered increased interest as the application of process analytical technologies (PAT) has expanded in the pharmaceutical industry. Pure component models such as iterative optimization technology (IOT) algorithms require only pure component spectra and no mixture samples as model inputs. However, the deployment of pure component models for PAT is limited by the lack of model diagnostics to establish confidence in model predictions and identify outliers. In the presented research, a novel diagnostic for use with IOT, called NAS-T, is proposed based on comparing the shape of net analyte signals (NAS) between the pure components and a sample mixture. Circular statistics were used in the analysis of NAS-T. The new diagnostic was applied in parallel with the partial least squares (PLS) Hotelling's T2 diagnostic to identify outlier near-infrared spectral samples from a set of pharmaceutical powder mixtures. The outliers detected by the NAS-T diagnostic included both chemical interferences (contamination) and physical interferences (process variations). Preprocessing the mixture sample spectra produced trends in the model diagnostics that were consistent between IOT and PLS. These results support the adoption of IOT and other pure component models for PAT applications in the pharmaceutical industry.

Development of an electrochemical sensor using carbon nanotubes and hydrophobic natural deep eutectic solvents for the detection of α-glucosidase activity in extracts of autochthonous medicinal plants

The present work describes for the first time the use of a hydrophobic natural deep eutectic solvent (H-NADES) as a dispersant for carboxylated nanotubes for the design and construction of an electrochemical sensor for the assay of α-glucosidase and its inhibitors. In this work, we used as the electrochemical probe the product of the enzymatic reaction, which consists of two redox groups and generates the analytical signal. The combination of de carboxylic multi-walled carbon nanotubes (MWCNTc) and the H-NADES of thymol and lactic acid (TLa) increases the electroactive surface area and promotes electron transfer of the electrode modified with carbon nanotubes. The electrochemical sensor enabled the detection of α-glucosidase in a range of 0.004–0.1 U mL−1 with a detection limit of 0.0013 U mL−1, which is lower than most existing methods. In addition, two α-glucosidase inhibitors, acarbose and quercetin, and two plant extracts, Schinus molle and Eugenia uniflora, were evaluated to assess the feasibility of screening potential antidiabetic drugs, and the IC50 values were 5.37 μg mL−1 and 5.28 μg mL−1. Thus, this sensing strategy represents the beginning of the incorporation of NADES in the development and design of novel sensors and their application in electrochemistry and medical analysis.

Discrimination of resected glioma tissues using surface enhanced Raman spectroscopy and Au@ZrO2 plasmonic nanosensor

Gliomas present one of the most prevalent malignant tumors related to the central nervous system. Surgical extraction is still a preferred route for glioma treatment. Nonetheless, neurosurgeons still have a considerable challenge to detect actual margins of the targeted glioma intraoperatively and correctly because of its great natural infiltration. Here we evaluated the possibility of using surface-enhanced Raman spectroscopy to analyze freshly resected brain tissues. The developed method is based on the application of Au@ZrO2 nanosensor. The plasmonic properties of the sensor were first tested on the analysis of Rhodamine 6G, where concentrations down to 10-7 mol/L can be successfully detected. We also compared the performance of the nanosensor with silver plasmonic nanoparticles, where similar results were obtained regarding the reduction of the fluorescence background and enhancement of the intensity of the measured analytical signal. However, application of silver nanospheres led to increased variations in spectral data due to its probable aggregation. Applied ZrO2@Au nanosensor thus dramatically lowers the fluorescence present in the Raman data, and considerably improves the quality of the measured signal. The developed method allows for rapid discrimination between the glioma's periphery and central parts, which could serve as a steppingstone toward highly precise neurosurgery.

Selective Deionization of Thin-Layer Samples Using Tandem Carbon Nanotubes-Polymeric Membranes.

Herein, we investigate the selective deionization (i.e., the removal of ions) in thin-layer samples (<100 μm in thickness) using carbon nanotubes (CNTs) covered with an ionophore-based ion-selective membrane (ISM), resulting in a CNT-ISM tandem actuator. The concept of selective deionization is based on a recent discovery by our group ( Anal. Chem. 2022, 94, 21, 7455-7459), where the activation of the CNT-ISM architecture is conceived on a mild potential step that charges the CNTs to ultimately generate the depletion of ions in a thin-layer sample. The role of the ISM is to selectively facilitate the transport of only one ion species to the CNT lattice. To estimate the deionization efficiency of such a process, a potentiometric sensor is placed less than 100 μm away from the CNT-ISM tandem, inside a microfluidic cell. This configuration helped to reveal that the selective uptake of ions increases with the capacitance of the CNTs and that the ISM requires a certain ion-exchanger capacity, but this does not further affect its efficiency. The versatility of the concept is demonstrated by comparing the selective uptake of five different ions (H+, Li+, Na+, K+, and Ca2+), suggesting the possibility to remove any cation from a sample by simply changing the ionophore in the ISM. Furthermore, ISMs based on two ionophores proved to achieve the simultaneous and selective deionization of two ion species using the same actuator. Importantly, the relative uptake between the two ions was found to be governed by the ion-ionophore binding constants, with the most strongly bound ion being favored over other ions. The CNT-ISM actuator concept is expected to contribute to the analytical sensing field in the sense that ionic interferents influencing the analytical signal can selectively be removed from samples to lower traditional limits of detection.

Limit of detection in qualitative analysis: Classification Analytical Signal approach

This paper presents a new methodology for the estimation of the limit of detection and other related parameters in qualitative analysis. Estimation of the detection limit is important in special cases when a small amount of an illegal ingredient can significantly affect the quality of the product or the presence of even a small amount of an extraneous analyte is significant. The approach is based on a recently introduced concept of an analytical signal in qualitative analysis, called the Classification Analytical Signal (CAS). A detailed algorithm to calculate the detection capability for a one-class classification method Data-Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is described and illustrated with two real-world examples. Both examples consider cases where the detection of low analyte concentrations is important and corresponding samples should be considered as non-compliant. The first case presents adulteration of wheat flour with talcum powder, where the samples were specially prepared with various concentrations of the analyte. The second case uses a publicly available dataset devoted to the estimation of tetracycline in blood serum. The latter case is used to compare the limits of detection estimated for multivariate calibration with those presented for qualitative analysis.

An emission based optical CO2 sensor fabricated on grating-like TiO2 substrates using HPTS

TiO2 is a widely used material in various applications, including photo-catalysis, sensing, and dye-sensitized solar cells. In this work, we presented a stable and sensitive CO2 sensor working on principle of emission signals of the HPTS dye immobilized on TiO2 substrates whose effectiverefractive indicesin the parallel and perpendicular grating orientations evidenced earlier. The HPTS exhibited 8.4-fold enhanced emission signal on the offered substrates with respect to the previously reported intensities recorded for the polymeric supports. The emission peaks of the HPTS exhibited CO2 induced intensity quenching when excited at 466 nm. Calibration sensitivity of the offered composites has been tested exposing the sensor materials to varying concentrations of the CO2 between 0.0 and 100.0 % pCO2 and correlating the measured fluorescence intensity with the corresponding CO2 levels. The dye revealed enhanced CO2 induced response exhibiting the I0/100 values of 11.9, 13.3, 11.2 and 6.5. We also recorded bi-exponential excited state lifetimes for the HPTS on four different test materials both in the absence and presence of the CO2. The emission based variations were followed as the analytical signal due to higher CO2-induced relative signal changes than that of the lifetime based variations. Herein we used the ion pair form of the HPTS in a protective chemical microenvironment and obtained considerable long term stability extending to16 mounts which can be attributed to the presence of the 1-butyl-3-methylimidazolium tetrafluoroborate as an internal buffering system in the sensing composition as well as the inert and robust structure of the substrate. The extremely high optical response, advantage of excitation with blue LEDs and long-term stability observed on grating-like TiO2 surfaces make the proposed system a promising design for the quantification of CO2 for further applications.

A differential strategy to enhance the anti-interference ability of molecularly imprinted electrochemiluminescence sensor with a semi-logarithmic calibration curve

The non-specific adsorption behaviors of various interferents on the surface of a molecularly imprinted polymer (MIP) are adverse for the selectivity of an MIP-based sensor, which can be overcome via a differential strategy by using the differential signal between MIP- and non-imprinted polymer (NIP)-based sensors. However, the normal differential mode is not suitable for the MIP-based sensors with non-linear calibration curves. Herein, an improved differential strategy is reported for an MIP-based sensor with a semi-logarithmic calibration curve, demonstrated by an electrochemiluminescence (ECL) sensor for dopamine (DA). Glassy carbon electrode (GCE) was modified by the mixture of g-C3N4, TiO2 nanoparticles (NPs) and carbon nanotubes (CNTs). MIP membrane for DA was fabricated on the surface of g-C3N4/TiO2NPs/CNTs/GCE using chitosan for film-forming, obtained MIP@GCE. To enhance the anti-interference ability of the MIP-based DA sensor, the difference between exponential functions ECL intensities of MIP@GCE and NIP@GCE is used as the analytical signal in the improved differential strategy. The differential signal was increased linearly with increasing DA concentration ranging from 10 pM to 0.10 μM, with the detection limit of 5.6 pM. The interference level of Cu2+ on DA determination in the improved differential mode is only 9.7% of that in the normal MIP mode. The improved differential strategy can be used in other MIP-based sensors with semi-logarithmic calibration curves.