Studies of Protein Binding to Biomimetic Membranes Using a Group of Uniform Materials Based on Organic Salts Derived From 8-Anilino-1-naphthalenesulfonic Acid.

Tuning the 8-anilino-1-naphthalenesulfonic acid (ANS) structure usually requires harsh conditions and long reaction times, which can result in low yields. Herein, ANS was modified to form an ANS group of uniform materials based on organic salts (GUMBOS), prepared with simple metathesis reactions and distinct cations, namely tetrabutylammonium (N4444), tetrahexylammonium (N6666), and tetrabutylphosphonium (P4444). These ANS-based GUMBOS were investigated as fluorescent probes for membrane binding studies with four proteins having distinct physicochemical properties. Liposomes of 1,2-dimyristoyl-sn-glycero-3-phosphocholine were employed as membrane models as a result of their ability to mimic the structure and chemical composition of cell membranes. Changes in fluorescence intensity were used to monitor protein binding to liposomes, and adsorption data were fitted to a Freundlich-like isotherm. It was determined that [N4444][ANS] and [P4444][ANS] GUMBOS have enhanced optical properties and lipophilicity as compared to parent ANS. As a result, these two GUMBOS were selected for subsequent protein-membrane binding studies. Both [N4444][ANS] and [P4444][ANS] GUMBOS and parent ANS independently reached membrane saturation within the same concentration range. Furthermore, distinct fluorescence responses were observed upon the addition of proteins to each probe, which demonstrates the impact of properties such as lipophilicity on the binding process. The relative maintenance of binding cooperativity and maximum fluorescence intensity suggests that proteins compete with ANS-based probes for the same membrane binding sites. Finally, this GUMBOS-based approach is simple, rapid, and involves relatively small amounts of reagents, making it attractive for high-throughput purposes. These results presented herein can also provide relevant information for designing GUMBOS with ameliorated properties.

Comparative analysis of electrochemical and optical sensors for detection of chronic wounds biomarkers: A review.

Chronic wounds (CW) present a significant healthcare challenge due to their prolonged healing time and associated complications. To effectively treat these wounds and prevent further deterioration, monitoring their healing progress is crucial. Traditional wound assessment methods relying on visual inspection and subjective evaluation are prone to inter-observer variability. Biomarkers play a critical role in objectively evaluating wound status and predicting healing outcomes, providing quantitative measures of wound healing progress, inflammation, infection, and tissue regeneration. Recent attention has been devoted to identifying and validating CW biomarkers. Various studies have investigated potential biomarkers, including growth factors, cytokines, proteases, and extracellular matrix components, shedding light on the complex molecular and cellular processes within CW. This knowledge enables a more targeted and personalized approach to wound management. Accurate and sensitive techniques are necessary for detecting CW biomarkers. Thus, this review compares and discusses the use of electrochemical and optical sensors for biomarker determination. The advantages and disadvantages of these sensors are highlighted. Differences in detection capabilities and characteristics such as non-invasiveness, portability, high sensitivity, specificity, simplicity, cost-effectiveness, compatibility with point-of-care applications, and real-time monitoring of wound biomarkers will be pointed out and compared. In summary, this work provides an overview of CW, explores the emerging field of CW biomarkers, and discusses methods for detecting these biomarkers, with a specific focus on optical and electrochemical sensors. The potential of further research and development in this field for advancing wound care and improving patient outcomes will also be noted.

Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models.

The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS2 quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

Chemometric-assisted surface-enhanced Raman spectroscopy for metformin determination using gold nanoparticles as substrate.

A fast, simple, and reliable method for determination of metformin was developed by coupling surface-enhanced Raman spectroscopy (SERS) with chemometric methods. This relayed on the utilization of a portable Raman spectrometer and of citrate stabilized gold nanoparticles (AuNPs) as substrate, to carry out the measurement of SERS scattering signals, thus assuring improved sensitivity. The obtained datasets were analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Upon optimization of the PLS model, in terms of latent variables, spectral region and pre-processing techniques, RMSECV and R2CV values of 0.42 mg/L and 0.94, respectively, were obtained. The optimized PLS regression model was further validated with the projection of commercial pharmaceutical samples, providing good results in terms of R2P (0.97), RE (4.54 %) and analytical sensitivity (2.13 mg/L).

A tutorial on multi-way data processing of excitation-emission fluorescence matrices acquired from semiconductor quantum dots sensing platforms.

This tutorial demonstrates how to exploit the second-order advantage on excitation-emission fluorescence matrices (EEFMs) acquired from sensing platforms based on analyte-triggered semiconductor quantum dots (QDs) fluorescence modulation (quenching/enhancing). The advantage in processing such second-order EEFMs data from complex samples, seeking successful quantification, is comprehensively addressed. It is worth emphasizing that, aiming to exploit the second-order advantage, the selection of the most appropriate advanced chemometric model should rely on the matching between the data structure and the physicochemical chemometric model assumption. In this sense, the achievement of second-order advantage after EEFMs' processing is extensively addressed throughout this tutorial taking into consideration three different analytical situations, each involving a specific data structure: i) parallel factor analysis (PARAFAC), which is applied in a real dataset stacked in a three-way data array containing a trilinear data structure acquired from QDs-based detection with non-selective species; ii) multivariate curve resolution - alternating least-squares (MCR-ALS), which is also employed in a real dataset arranged in an augmented data matrix containing non-trilinear data structure acquired from QDs-based detection with a single breaking mode caused by background signals; iii) unfolded partial least-squares with residual bilinearization (U-PLS/RBL), which is applied in a dataset containing non-trilinear data acquired from a classical fluorescence system with two breaking modes caused by inner filter effect (IFE) in both instrumental modes (excitation and emission). The latter challenging data structure can be acquired via fluorescence quenching from IFE-based sensing platforms and chemometrically handled in two main steps. First, a set of calibration EEFMs data is converted into an unfolded data matrix during the unfolding process, followed by applying U-PLS model. Second, a post-calibration procedure using RBL analysis is applied to a test sample of EEFM maintained in its matrix form, in order to handle potential interferents. In the last section, the state-of-the-art of second-order EEFMs data acquired from semiconductor QDs-based sensing platforms and coupled to multi-way fluorescence data processing to accomplish a successful quantification, even with substantial interfering species, is critically reviewed.

Cellulose-based hydrogel on quantum dots with molecularly imprinted polymers for the detection of CA19-9 protein cancer biomarker.

Molecularly imprinted polymers MIPs were successfully assembled around quantum dots (QDs), for the detection of the protein biomarker CA19-9 associated to pancreatic cancer (PC). These imprinted materials MIP@QDs were incorporated within the cellulose hydrogel with retention of its conformational structure inside the binding cavities. The concept is to use MIPs which function as the biorecognition elements, conjugated to cadmium telluride QDs as the sensing system. The excitation wavelength was set to 477 nm and the fluorescence signal was measured at its maximum intensity, with an emission range between 530 and 780 nm. The fluorescence quenching of the imprinted cellulose hydrogels occurred with increasing concentrations of CA19-9, showing linearity in the range 2.76 × 10 -2 - 5.23 × 10 2 U/ml, in a 1000-fold diluted human serum. Replicates of the imprinted hydrogel show a linear response below the cut-off values for pancreatic cancer diagnosis (< 23 U/ml), a limit of detection of 1.58 × 10 -3 U/ml and an imprinting factor (IF) of 1.76. In addition to the fact that the imprinted cellulose hydrogel displays good stability and selectivity towards CA19-9 when compared with the non-imprinted controls, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical detection method towards ranges within clinical significance. This fact shows potential for the imprinted hydrogel to be applied as a sensitive, low-cost format for point-of-care tests (PoCTs).

Protein discrimination using erythrosin B-based GUMBOS in combination with UV-Vis spectroscopy and chemometrics.

GUMBOS (Group of Uniform Materials Based on Organic Salts) have recently emerged as interesting materials for protein analysis due to their unique features and high tunability. In this regard, four novel erythrosin B (EB)-based GUMBOS were synthesized and their potential to discriminate among proteins with distinct properties (e.g., size, charge, and hydrophobicity) was assessed. These solid-phase materials were prepared using a single-step metathesis reaction between EB and various phosphonium and ammonium cations, namely tetrabutylphosphonium (P4444+), tributylhexadecylphosphonium (P44416+), tetrabutylammonium (N4444+), and benzyldimethylhexadecylammonium (BDHA+). Subsequently, the effect of pH (3.0, 4.5, and 6.0) and reaction time (5, 10, and 15 min) on the discriminatory power of synthesized GUMBOS was evaluated. Absorption spectra resulting from the interaction between EB-based GUMBOS and proteins were analyzed using partial least squares discriminant analysis (PLSDA). Unlike time, the pH value was determined to have influence over GUMBOS discrimination potential. Correct protein assignments varied from 86.5% to 100.0%, and the best discriminatory results were observed for [P4444]2[EB] and [N4444]2[EB] at pH 6.0. Additionally, these two GUMBOS allowed discrimination of protein mixtures containing different ratios of albumin and myoglobin, which appeared as individualized clusters in the PLSDA scores plots. Overall, this study showcases EB-based GUMBOS as simple synthetic targets to provide a label-free, cost-effective, rapid, and successful approach for discrimination of single proteins and their mixtures.

Photoluminescent and visual determination of ibandronic acid using a carbon dots/AgInS2 quantum dots ratiometric sensing platform.

A sensing platform combining carbon dots (CDs, with blue emission) and thiomalic acid (TMA)-capped AgInS2 quantum dots (QDs, with orange emission) was developed aiming the photoluminescence (PL) ratiometric determination of ibandronic acid (IBAN), a bisphosphonate pharmaceutical. The ternary AgInS2 QDs were used for IBAN probing, undergoing a concentration-related PL quenching in its presence, whilst the PL of CDs remained practically unaffected due to its chemical inertness towards the antiresorptive drug, provided an intrinsic self-reference fluorophore. In addition, a visual sensing approach was also proposed, employing for the first time ternary QDs. This relied on RGB images acquired by means of a digital camera and seek the development of a rapid IBAN screening test. The developed sensing platforms were employed for IBAN determination in samples with pharmaceutical interest providing good results, in accordance to the reported IBAN levels, and obtaining recovery values between 98 and 103%.

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms.

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS2 ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R2P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Comparison of near infrared spectroscopy and Raman spectroscopy for the identification and quantification through MCR-ALS and PLS of peanut oil adulterants.

Peanut oil is considered one of the best frying oils, and, consequently there is an increasing worldwide demand. This has led to adulteration practices with unhealthy, synthetic or less expensive oils which raises concerns related with public health safety. Therefore, there is a high need for rapid, versatile, low-cost and reliable analytical methods, such as vibrational spectroscopic techniques, capable of identifying and quantifying the respective adulteration. The objective of this work focused on the application of two different vibrational spectroscopic techniques (NIR and Raman spectroscopy) for the qualitative and quantitative analysis of two adulterants in pure peanut oil, namely corn oil and vegetable oil. For the quantitative analysis two chemometric methods, namely PLS and MCR-ALS, were compared while for the qualitative analysis only MCR-ALS was tested. The analysis of peanut oil adulteration was performed by adding each adulterant individually and also by blending the peanut oil with both adulterants simultaneously. A total of 69 samples were analyzed, which was comprised by two sets of 20 samples each containing just one adulterant and another set of 29 samples containing both adulterants. Several pre-processing techniques were tested. The qualitative analysis performed by MCR-ALS allowed the identification of all the adulterants using both NIR and Raman spectra, with correlation coefficients higher than 0.99. For the quantification, none of the chemometric methods as well as the vibrational spectroscopic techniques tested showed significant better results. Nonetheless, the determination coefficients and the relative percentage errors for the validation samples for most of the developed models were higher than 0.98 and lower than 15%, respectively. Concluding, MCR-ALS was capable of correctly extracting the spectral profiles of all the adulterants in very complex mixtures (as the pure spectra of the adulterants and peanut oil are very similar) and both MCR-ALS and PLS were able to quantify the adulteration with low RE. To the best of our knowledge, it was the first time that MCR-ALS was used for the qualitative analysis of peanut oil adulteration (with all adulterants added simultaneously) and MCR-ALS and PLS were compared for the quantification of peanut oil adulteration using both NIR and Raman spectroscopy.

Development of an automated yeast-based spectrophotometric method for toxicity screening: Application to ionic liquids, GUMBOS, and deep eutectic solvents.

Saccharomyces cerevisiae has been used as a eukaryotic model organism for studying the toxic effects of various compounds. In this context, an automated spectrophotometric method based on the enzymatic reduction of methylene blue dye to a colorless product by living yeast cells was implemented in a sequential injection analysis system. Loss of yeast viability/impaired metabolic activity was monitored by an increase in optical density at 664 nm. To prove the usefulness of this approach, the toxicity of ILs (ionic liquids), GUMBOS (group of uniform materials based on organic salts), and DESs (deep eutectic solvents) was examined. Differences obtained between IC50 values confirmed the impact of structural elements on each compounds' toxicity. While DESs appeared to be less toxic than ILs, GUMBOS were found to be among the most toxic compounds to yeast cells and thus can be viewed as promising antimicrobial candidates. The automated methodology showed satisfactory repeatability and reproducibility (RSD < 9%), which is in good agreement with Green Chemistry principles. In fact, the method required consumption of only 40 µL of reagents and produced less than 2 mL of effluents per cycle. Thus, the developed assay can be used as an alternative tool for toxicity screening.

Imprinted Fluorescent Cellulose Membranes for the On-Site Detection of Myoglobin in Biological Media.

In this work, the conjugation of molecularly imprinted polymers (MIPs) to quantum dots (QDs) was successfully applied in the assembly of an imprinted cellulose membrane [hydroxy ethyl cellulose (HEC)/MIP@QDs] for the specific recognition of the cardiac biomarker myoglobin (Myo) as a sensitive, user-friendly, and portable system with the potential for point-of-care (POC) applications. The concept is to use the MIPs as biorecognition elements, previously prepared on the surface of semiconductor cadmium telluride QDs as detection particles. The fluorescent quenching of the membrane occurred with increasing concentrations of Myo, showing linearity in the interval range of 7.39-291.3 pg/mL in a1000-fold diluted human serum. The best membrane showed a linear response below the cutoff values for myocardial infarction (23 ng/mL), a limit of detection of 3.08 pg/mL, and an imprinting factor of 1.65. The incorporation of the biorecognition element MIPs on the cellulose substrate brings an approach toward a portable and user-friendly device in a sustainable manner. Overall, the imprinted membranes display good stability and selectivity toward Myo when compared with the nonimprinted membranes (HEC/NIP@QDs) and have the potential to be applied as a sensitive system for Myo detection in the presence of other proteins. Moreover, the conjugation of MIPs to QDs increases the sensitivity of the system for an optical label-free detection method, reaching concentration levels with clinical significance.

Determination of atenolol based on the reversion of the fluorescence resonance energy transfer between AgInS2 quantum dots and Au nanoparticles.

The present work focused on the development of a fluorescence resonance energy transfer (FRET)-based sensing platform for the monitoring of atenolol in pharmaceutical formulations. The implemented approach involved the assembly of d-penicillamine-capped AgInS2/ZnS quantum dots (QDs), as energy donors, and gold nanoparticles (AuNPs) as acceptors and the establishment of electrostatic interaction between both capping ligands at the nanoparticle surface, which induced the inhibition of the ternary QD photoluminescence (PL). The presence of a ZnS shell around the ternary QD core and the use of cysteamine (CA) as the AuNP capping ligand, instead of the typical citrate, allowed a more efficient FRET process to occur. The ability of Cd-free ternary QDs to be used as a sensing element in FRET-based assays was demonstrated, emphasizing the advantages relative to the common Cd-based QDs, when seeking the implementation of more environmentally friendly and less toxic analytical methodologies. The influence of several ß-blocker drugs on the FRET donor-acceptor assemblies was thoroughly assessed. Atenolol and nadolol caused the aggregation of CA-AuNPs via hydrogen bonding interactions which reduced the spectral overlap between the donor and acceptor, impairing the FRET process and consequently the emission of the QDs was restored. Under the optimized conditions, the obtained results exhibited a linear relationship between the QD PL recovery signal and atenolol concentration of up to 11.22 mg L-1 with a detection limit of 1.05 mg L-1. This FRET sensing platform was successfully applied in the determination of atenolol in pharmaceutical formulations with recovery values ranging from 97.4 to 104.3%.

GUMBOS and nanoGUMBOS in chemical and biological analysis: A review.

GUMBOS (group of uniform materials based on organic salts) is a novel class of materials that exhibits similar features to those of ionic liquids, but have melting points between 25 and 250 °C. GUMBOS can be easily converted into nanomaterials (nanoGUMBOS), with advantages of working at nanoscale. Due to the huge number of possible cation-anion combinations, these materials can be multifunctional and designed for a specific task. This review highlights the possibility of fine-tuning GUMBOS physical and chemical properties in view of changing their ionic counterparts. Their outstanding potential for analytical applications is shown through recent developments in areas such as sensing, and solid-phase extraction. Available methods for synthesis of nanoGUMBOS, and their different outcomes in shapes and optical properties are described, with pros and cons being outlined. Finally, an analysis is made of opportunities and challenges faced by this class of organic ionic materials.

Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence: A short communication.

The present short communication reports a promising analytical method for authentication of milk based on first-order near-infrared (NIR) spectroscopic data coupled to data driven soft independent modeling of class analogy (DD-SIMCA). This one-class classifier was able to correctly classify all samples of genuine milk powder as members of the target class from samples of milk powder adulterated with melamine and sucrose in a concentration range of 0.8-2% (w/w) and 1-3% (w/w), respectively. Multivariate curve resolution - alternating least-squares (MCR-ALS) was applied as a complementary chemometric model to DD-SIMCA aimed at retrieving pure profiles, allowing to identify the chemical composition of samples properly attributed in the target class or not, providing further investigation from forensic point of view. In order to extend the prime focus of the present report, which was aimed at developing an appropriate chemometric model for authentication purposes, the quantification analysis was also performed. This was done by successful bilinear data decomposition of NIR spectra into pure profiles for the contributing components contained in the system studied (milk and adulterants), allowing to quantify analytes with strong overlapping profiles, even in the presence of an uncalibrated interferent, as demonstrated in this short communication using MCR-ALS under various constraints in order to decrease the rotational ambiguity.

Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II).

This work focused on the combination of CdTe and AgInS2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R2P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7-9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.

MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering.

BACKGROUND: In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering. RESULTS: Production of ethanol by the widely used cell factory Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. CONCLUSIONS: The multi-objective programming framework we developed, called MOMO, is open-source and uses POLYSCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. MOMO is available at http://momo-sysbio.gforge.inria.fr.

Plastic antibodies tailored on quantum dots for an optical detection of myoglobin down to the femtomolar range.

A highly sensitive fluorescence detection probe was developed by tailoring plastic antibodies on the external surface of aqueous soluble quantum dots (QDs). The target was Myoglobin (Myo), a cardiac biomarker that quenched the intrinsic fluorescent emission of cadmium telluride (CdTe) QDs capped with mercaptopropionic acid (CdTe-MPA-QDs). The QDs were incubated with the target protein and further modified with a molecularly-imprinted polymer (MIP) produced by radical polymerization of acrylamide and bisacrylamide. The main physical features of the materials were assessed by electron microscopy, dynamic light scattering (DLS), UV/Vis spectrophotometry and spectrofluorimetry. The plastic antibodies enabled Myo rebinding into the QDs with subsequent fluorescence quenching. This QD-probe could detect Myo concentrations from 0.304 to 571 pg/ml (50.6 fM to 95 pM), with a limit of detection of 0.045 pg/ml (7.6 fM). The proposed method was applied to the determination of Myo concentrations in synthetic human serum. The results obtained demonstrated the ability of the modified-QDs to determine Myo below the cut-off values of myocardial infarction. Overall, the nanostructured MIP-QDs reported herein displayed quick responses, good stability and sensitivity, and high selectivity for Myo, offering the potential to be explored as new emerging sensors for protein detection in human samples.

Multiplexed analysis combining distinctly-sized CdTe-MPA quantum dots and chemometrics for multiple mutually interfering analyte determination.

Semiconductor quantum dots (QDs) have demonstrated a great potential as fluorescent probes for heavy metals monitoring. However, their great reactivity, whose tunability could be difficult to attain, could impair selectivity yielding analytical results with poor accuracy. In this work, the combination in the same analysis of multiple QDs, each with a particular ability to interact with the analyte, assured a multi-point detection that was not only exploited for a more precise analyte discrimination but also for the simultaneous discrimination of multiple mutually interfering species, in the same sample. Three different MPA-CdTe QDs (2.5, 3.0 and 3.8nm) with a good size distribution, confirmed by the FWHM values of 48.6, 55.4 and 80.8nm, respectively, were used. Principal component analysis (PCA) and partial least squares regression (PLS) were used for fluorescence data analysis. Mixtures of two MPA-CdTe QDs, emitting at different wavelength namely 549/566, 549/634 and 566/634nm were assayed. The 549/634nm emitting QDs mixture provided the best results for the discrimination of distinct ions on binary and ternary mixtures. The obtained RMSECV and R2CV values for the binary mixture were good, namely, from 0.01 to 0.08mgL-1 and from 0.74 to 0.89, respectively. Regarding the ternary mixture the RMSECV and R2CV values were good for Hg(II) (0.06 and 0.73mgL-1, respectively) and Pb(II) (0.08 and 0.87mg L-1, respectively) and acceptable for Cu(II) (0.02 and 0.51mgL-1, respectively). In conclusion, the obtained results showed that the developed approach is capable of resolve binary and ternary mixtures of Pb (II), Hg (II) and Cu (II), providing accurate information about lead (II) and mercury (II) concentration and signaling the occurrence of Cu (II).

Physical and chemical immobilization of choline oxidase onto different porous solid supports: Adsorption studies.

This work carries out for the first time the comparison between the physical and chemical immobilization of choline oxidase onto aminated silica-based porous supports. The influence on the immobilization efficiency of concentration, pH, temperature and contact time between the support and choline oxidase, was evaluated. The immobilization efficiency was estimated taking into consideration the choline oxidase activity, which was assessed by using cadmium telluride (CdTe) quantum dots (QDs), obtained by hydrothermal synthesis, as photoluminescent probes. Hydrogen peroxide produced by enzyme activity was capable of quenching CdTe QDs photoluminescence. The magnitude of the PL quenching process was directly related with the enzyme activity. By comparing the chemical process with the physical adsorption, it was observed that the latter provided the highest choline oxidase immobilization. The equilibrium data were analyzed using Langmuir and Freundlich isotherms and kinetic data were fitted to the pseudo-first-order and pseudo-second-order models. Thermodynamic parameters, such as Gibbs free energy and entropy were also calculated. These results will certainly contribute to the development of new sensing schemes for choline, taking into account the growing demand for its quantification in biological samples.