Modeling and optimization of the ratio of fluorophores: a step towards enhancing the sensitivity of ratiometric probes.

In the ratiometric fluorescent (RF) strategy, the selection of fluorophores and their respective ratios helps to create visual quantitative detection of target analytes. This study presents a framework for optimizing ratiometric probes, employing both two-component and three-component RF designs. For this purpose, in a two-component ratiometric nanoprobe designed for detecting methyl parathion (MP), an organophosphate pesticide, yellow-emissive thioglycolic acid-capped CdTe quantum dots (Y-QDs) (analyte-responsive), and blue-emissive carbon dots (CDs) (internal reference) were utilized. Mathematical polynomial equations modeled the emission profiles of CDs and Y-QDs in the absence of MP, as well as the emission colors of Y-QDs in the presence of MP separately. In other two-/three-component examples, the detection of dopamine hydrochloride (DA) was investigated using an RF design based on blue-emissive carbon dots (B-CDs) (internal reference) and N-acetyl L-cysteine functionalized CdTe quantum dots with red/green emission colors (R-QDs/G-QDs) (analyte-responsive). The colors of binary/ternary mixtures in the absence and presence of MP/DA were predicted using fitted equations and additive color theory. Finally, the Euclidean distance method in the normalized CIE XYZ color space calculated the distance between predicted colors, with the maximum distance defining the real-optimal concentration of fluorophores. This strategy offers a more efficient and precise method for determining optimal probe concentrations compared to a trial-and-error approach. The model's effectiveness was confirmed through experimental validation, affirming its efficacy.

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality of environmental water and assessing the potential risks associated with industrial waste. This study introduces a single-well fluorometric sensor that utilizes orange emissive thioglycolic acid stabilized CdTe quantum dots (TGA-QDs) and blue emissive carbon dots (CDs) to detect and differentiate between various chromium species, such as Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-). The variations of fluorescence spectra of the proposed probe upon chromium species addition were analyzed using machine learning techniques such as linear discriminant analysis and partial least squares regression as a classification and multivariate calibration technique, respectively. Linear discriminant analysis (LDA) demonstrated exceptional accuracy in differentiating single-component and bicomponent samples. Additionally, the findings from the partial least squares regression (PLSR) showed that the sensor created has strong linearity within the 1.0-100.0, 1.0-100.0, and 0.1-15 µM range for Cr2O72-, CrO42-, and Cr3+, respectively. Furthermore, appropriate detection limits were successfully achieved, which were 2.6, 2.9, and 0.7 µM for Cr2O72-, CrO42-, and Cr3+, respectively. Ultimately, the successful capability of the sensing platform in the identification and quantification of chromium species in environmental water samples provides innovative insights into general speciation analytics.

Fast and Facile Etching of Gold Nanorods by N-Halosuccinimides: Toward Multicolorimetric Identification and Quantification of 20 Natural Amino Acids.

Gold nanorods (AuNRs) have recently become fascinating chromophores in the field of colorimetric sensing because of their eye-catching rainbow colors along with the high dimensionality of their optical profile. The etching of AuNRs using an analyte-sensitive oxidizing agent is particularly an attractive tool not only for adjusting their plasmonic behavior through altering their aspect ratio but also for correlating the observed signal with the identity and concentration of the analyte. However, the deployment of this strategy in the field of sensing has been seriously hindered by various factors ranging from slow etching kinetics and the need for nonambient temperatures to low degrees of controllability along with the high toxicity of the etchants. To resolve these challenges, the present study aims to introduce the outstanding potentials of two inexpensive mild oxidants comprising N-bromosuccinimide (NBS) and N-chlorosuccinimide (NCS) in the highly fast and controllable etching of AuNRs at room temperature. By controlling the concentration of the etchant and the pH of the medium, the longitudinal and transversal peaks could be well adjusted with nanometer precision. In an attempt to elucidate the etching mechanism, the effects of various parameters including the etchant concentration and pH, as well as the kinetics of the etching process were thoroughly investigated. After all, the capability of NBS in decarboxylating the amino acids was further exploited in the design of an all-inclusive multicolorimetric sensor array based on the etching of AuNRs for the sensitive quantification and highly accurate discrimination of all 20 amino acids in the micromolar range. To this end, the acquired data set was analyzed by two machine learning techniques including partial least-squares regression (PLSR) and linear discriminant analysis (LDA). The versatility of N-halosuccinimide reactions with various categories of organic compounds underlies ample opportunities for the design of diverse multicolorimetric sensors, further glamorizing the prospect of this approach.

Multiplex Detection of Biogenic Amines for Meat Freshness Monitoring Using Nanoplasmonic Colorimetric Sensor Array.

Biogenic amines (BAs) were presented as significant markers for the evaluation of the spoilage of meat and meat products. In this work, a colorimetric sensor array was developed for the discrimination and detection of spermine (SP), spermidine (SD), histamine (HS), and tryptamine (TP) as important BAs in food assessment. For this aim, two important spherical plasmonic nanoparticles, namely gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs), were utilized as the sensing elements of the probes. The cross-reactive interaction of the target biogenic amines and the plasmonic nanoparticles caused the aggregation-induced UV-Vis spectra changes, which were accompanied by visual color variation in the solution. The collected responses were analyzed by principal component analysis-linear discrimination analysis (PCA-LDA) to classify the four BAs. This colorimetric sensor array can also discriminate between the individual BAs and their mixture accurately. Partial least squares regression (PLS-R) was also utilized for quantitative analysis of the BAs. The wide linear concentration ranges of 0.1-10.0 µM for the four BAs and desirable figures of merits (FOMs) showed the potential of the developed sensor for quantitative detection of the BAs. Finally, the practical ability of the developed probe was studied by the determination of the BAs in the meat samples, which successfully proved the potential of the colorimetric sensor array in a food sample.

Machine Learning-Assisted Colorimetric Assay Based on Au@Ag Nanorods for Chromium Speciation.

The oxidation state of an element significantly controls its toxicological impacts on biological ecosystems. Therefore, design of robust sensing strategies for multiplex detection of species with respect to their oxidation states or bonding conditions, i.e., chemical speciation, is quite consequential. Chromium (Cr) species are known as the most abundant inorganic groundwater pollutants and can be quite harmful to human health depending on their oxidation states. In the present study, a multicolorimetric probe based on silver-deposition-induced color variations of gold nanorods (AuNRs) was designed for identification and quantification of Cr species including Cr (III) and Cr (VI) (i.e., CrO42- and Cr2O72-) in water samples. In fact, the presence of Cr species leads to inhibition of the silver metallization of AuNRs to various degrees depending on the concentration and identity of the analyte. This process is accompanied by the blue shift of the longitudinal peak which results in sharp-contrast rainbow-like color variations, thereby providing great opportunity for highly accurate visual detection. The gathered dataset was then statistically analyzed using two pattern recognition and regression machine learning techniques. In particular, linear discriminant analysis was used as a classification method to discriminate the unicomponent and mixture samples of Cr species with 100% accuracy. Then, a well-known multivariate calibration technique called partial least-squares regression was employed for quantitative analysis of Cr species. Responses were linearly related to Cr species concentrations over a wide range of 10.0-1000.0, 1.0-200.0, and 1.0-200.0 µmol L-1 with detection limits of 37.7, 8.7, and 2.9 µmol L-1 for Cr3+, CrO42-, and Cr2O72-, respectively. The practical applicability of the multicolorimetric probe was successfully evaluated by analyzing Cr species in several water specimens comprising tap water, mineral water, river water, and seawater. Above all, the vivid rainbow color tonality of the proposed assay further improves the accuracy of the naked eye detection, making it a practical platform for on-site monitoring of Cr contamination.

Nanoplasmonic Decoration of Sacrificial Bacteria: Directing Interparticle Coupling Toward Multiplex Analysis of Antiseptic Alcohols.

Engineering interparticle plasmon coupling through controlling the assembly of plasmonic NPs onto the surface of sacrificial substrates is quite promising for establishing inherently absent selectivity or sensitivity toward a particular analyte. Herein, we introduce a robust sensor array strategy based upon the assembly of gold nanoparticles (AuNPs) on the cysteamine-modified surface of two Gram-positive probiotic bacteria, i.e., Lactobacillus reuteri (LBR) and Bifidobacterium lactis (BFL), as potential sacrificial substrates, for discrimination and quantification of antiseptic alcohols (AAs) comprising methanol, ethanol, and isopropanol. In fact, the damage of the bacterial membrane upon exposure to the foregoing alcohols inhibits the assembly of AuNPs, thereby precluding color variations from red to blue. Unequal resistance of the bacterial membranes against damage by the alcohols underlies independent response patterns for each analyte. The supervised classification of visible spectra and RGB data by Linear Discriminant Analysis (LDA) revealed the remarkable potential of the designed sensor array in differentiating single-component and multicomponent samples of AAs. Moreover, the Partial Least Square Regression (PLSR) technique exhibited excellent applicability to multivariate calibration of both spectral and RGB data. The intriguing attributes of the implemented approach not only hold great potential in the authentication and quality assessment of alcohol-based products but open up a new prospect for deployment of sacrificial substrates in the design of interparticle coupling-based sensors.

Toward visual chiral recognition of amino acids using a wide-range color tonality ratiometric nanoprobe.

Chiral recognition has long been a challenging issue to deal with in biological systems, drug design and food authentication. Implementing nanoparticle-based probes with intrinsic or induced chirality in this field has addressed several issues concerning sensitivity, reliability, rapidness and the cost of chiral sensing platforms. Yet, research into chiral nanoprobes that can be used for visual monitoring of chiral substances is still in its infancy. As part of this study, a visual chiral recognition platform has been developed in which a combination of blue-emitting carbon dots (BCDs) and mercaptopropionic acid-capped CdTe quantum dots (MPA-QDs) with inherent chiroptical activity were employed for enantiomeric detection. The ratiometric probe displayed unique fluorescence response patterns in the presence of arginine (Arg) and histidine (His) enantiomers. Upon addition of l-amino acids, successive enhancement and quenching of emission intensity as well as a red-shift in emission wavelength of MPA-QDs were observed. The emission color of the nanoprobe changed from green to pink-red and green to brick-red red by increasing the concentration of L-Arg and L-His, respectively. In contrast, their d-amino acid equivalents have a negligible influence on the emission color and fluorescence signal of the developed nanoprobe. Due to the enantioselective vibrant color changes of the nanoprobe, RGB analysis was applied for the determination of enantiomeric excess (ee) in racemic mixture with satisfactory results, allowing smartphone-based onsite visual evaluation of ee (%). Circular dichroism, lifetime, size distribution and ζ-potential measurements were employed to study the chiroselective responses. First-principle calculations were also carried out with density functional theory (DFT) to confirm the experimental observation. Furthermore, chiroselective response patterns of the ratiometric nanoprobe were manipulated to construct a logic gate system mimicking AND, OR, and INHIBIT functions. The capability of the proposed chiral platform in visual monitoring of the fraction of enantiomers in racemic mixtures has a great potential for rapid and onsite visual discrimination of chiral compounds in the field of clinical diagnostics and drug analysis.

Determination of spermine and spermidine in meat with a ratiometric fluorescence nanoprobe and a combinational logic gate.

A ratiometric fluorescent nanoprobe is developed with a wide color variation for visual determination of spermine (SP) and spermidine (SD) in meat samples. The green emission provided from the combination of yellow emissive quantum dots and blue emissive carbon dots turns into pink when SP or SD are present. The results show that the developed sensor has good linearity in the range of 0.5-10 and 0.5-80 µM for SP and SD and suitable detection limits were achieved including 0.2 and 2.1 µM for SP and SD. The probe was highly selective in the presence of amino acids and other biogenic amines. RGB indices were extracted to build a combinational logic gate for visual and simultaneous detection of SP and SD. The dual functional logic gate was easy to design and convenient to operate. Finally, a portable sensor was fabricated for visual, rapid and on-site assessment of meat freshness.

A Smartphone-Based Fluorescent Electronic Tongue for Tracing Dopaminergic Agents in Human Urine.

The importance of tracing dopaminergic agents in the progression assessment of Parkinson's disease has boosted the demand for fast, sensitive, and real-time multi-analyte detection. Herein, visual and fingerprint fluorimetric patterns have been created by an optical sensor array to simultaneously detect and discriminate among levodopa, carbidopa, benserazide, and entacapone, as important dopaminergic agents. A dual emissive nanoprobe consisting of red quantum dots and blue carbon dots with an overall pink emission has been fabricated to provide unique emission patterns in the presence of the target analytes. The sensor elements in the array come from it's differential response in the absence and presence of cetyltrimethylammonium bromide under alkaline conditions. A smartphone camera was used to take photos from the solutions in the wells. Distinct changes in the spectral profiles along with vivid and concentration-dependent color variations led to visual discrimination of dopaminergic agents in a broad concentration range. The results of linear discriminant analysis revealed great discrimination accuracies. Different concentrations of the target analytes were excellently recognized in human urine. The high sensitivity of the array, which is a bonus to rapid, on-site, and visual discrimination of dopaminergic agents, holds great promise for routine analysis of real-world clinical samples.

Providing Multicolor Plasmonic Patterns with Au@Ag Core-Shell Nanostructures for Visual Discrimination of Biogenic Amines.

Biogenic amines (BAs) are known as substantial indicators of the quality and safety of food. Developing rapid and visual detection methods capable of simultaneously monitoring BAs is highly desired due to their harmful effects on human health. In the present study, we have designed a multicolor sensor array consisting of two types of gold nanostructures (i.e., gold nanorods (AuNRs) and gold nanospheres (AuNSs)) for the discrimination and determination of critical BAs (i.e., spermine (SM), tryptamine (TT), ethylenediamine (EA), tyramine (TR), spermidine (SD), and histamine (HT)). The design principle of the probe was based on the metallization of silver ions on the surface of AuNRs and AuNSs in the presence of BAs, forming Au@Ag core-shell nanoparticles. Changes in the surface composition, size, and aspect ratio of AuNSs and AuNRs induced a blue shift in the plasmonic band, which was accompanied by sharp and rainbowlike color variations in the solution. The collected data were visually assessed and statistically analyzed by various data visualization and pattern recognition methods. Namely, linear discriminant analysis (LDA) and partial least squares (PLS) regression were employed for the qualitative and quantitative determination of BAs. The responses were linearly correlated to the concentrations of BAs in a wide range of 10-800, 20-800, 40-800, 40-800, 60-800, and 80-800 µmol L-1 with the limit of detections of 2.46, 4.79, 8.58, 14.26, 10.03, and 27.29 µmol L-1 for SD, SM, TT, HT, EA, and TR, respectively. Finally, the practical applicability of the sensor array was investigated by the determination of BAs in meat and fish samples by which the potential of the probe for on-site determination of food freshness/spoilage was successfully verified.

Application of NaYF4:Yb/Er/Tm UCNPs in Array-Based Sensing of Neurotransmitters: From a Single Particle to a Multichannel Sensor Array.

A novel multichannel sensor array has been designed using a single, yet multiemissive lanthanide-doped upconversion nanoparticle (UCNP). The energy levels of lanthanide ions gave rise to several emission bands which were exploited as individual sensor elements for the recognition of four important neurotransmitters (NTs): dopamine, norepinephrine, levodopa, and serotonin. At alkaline conditions, the oxidation products of these NTs quenched the fluorescence emissions of UCNPs with different quenching degrees. The resulting fingerprint multichannel emission profiles from NaYF4:Yb/Er/Tm UCNPs allowed the discrimination of NTs with excellent accuracy. The recognition was further verified in artificial cerebrospinal fluid, as a complex biological media. We believe that the designed UCNP-based multichannel sensor array offers innovative insights into the discrimination of various chemical signatures using a single measurement.

Laser irradiation affects the biological identity and cellular uptake of plasmonic nanoparticles.

The biological identity of nanoparticles (NPs) is defined by a protein layer formed on their surface, called protein corona (PC), once they meet the biological milieu. Any change in the PC composition may influence the biological fate of NPs. The PC composition is strongly dependent on several parameters including the physicochemical properties of NPs, and biological and environmental factors. As one of the main features of plasmonic NPs is their capacity to induce local heating by laser irradiation, we hypothesized that laser irradiation may change the biological identity of NPs and therefore alter their biological fate. To test this hypothesis, here we investigated the effects of either simultaneous or sequential laser irradiation on the conformations of a few proteins selected from two main categories of plasma proteins (i.e. human serum albumin and human fibrinogen) on the surfaces of gold nanorods (AuNRs). The outcomes revealed a significant role of laser irradiation on conformational changes of fibrinogen compared to albumin. Moreover, the effects of plasmonic heating - at various times - on the achieved corona composition from interactions of AuNRs and human plasma with various concentrations were monitored. Consequently, the cellular uptake of the corona coated AuNRs was measured in two cell types: malignant (MCF-7) and normal (MCF-10A) breast cell lines. The results demonstrated a substantial reduction in the cellular uptake of AuNRs in response to an increase in the laser irradiation time, especially in MCF-10A. Our results may pave the way for a mechanistic understanding of the biological identity of plasmonic NPs which in turn can help their safe and efficient clinical translations.

Chemiluminometric fingerprints for identification of plasmonic nanoparticles.

Development of a convenient and inexpensive method for identification and detection of nanoparticles (NPs) is of great interest. In this work, we have developed a novel and simple chemiluminescence based sensor array, with its sensing mechanism mimicking that of olfactory and gustatory systems for discriminating a set of NPs. The proposed method is based on the enhancement effect of NPs on luminol-oxidant CL intensity by their catalytic effect. Three kinds of oxidant including H2O2, AgNO3, and K3Fe(CN)6 were used as sensor elements and NPs exhibited diverse enhancing responses to different oxidant-luminol CL systems producing unique response patterns that were identified through heat map and chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). Five NPs have been well distinguished at various concentrations. In addition, this method clearly revealed a linear relationship between CL signal values and the concentrations of NPs for the quantitative detection of NPs. We believe that this type of CL sensor array can open a new way for facile discrimination and detection of different kinds of NPs.

Exploring Cellular Interactions of Liposomes Using Protein Corona Fingerprints and Physicochemical Properties.

To control liposomes fate and transport upon contact with biofluids, it is essential to consider several parameters affecting the synthetic and biological identity of liposomes, as well as liposome-protein corona (PC) aspects. As a powerful tool in this data mining adventure, quantitative structure-activity relationship (QSAR) approach is used to correlate physicochemical properties of liposomes and their PC fingerprints to multiple quantified biological responses. In the present study, the relationship between cellular interactions of a set of structurally diverse liposomal formulations and their physicochemical and PC properties has been investigated via linear and nonlinear QSAR models. Significant parameters affecting cellular uptake and cell viability of liposomes in two important cancer cell lines (PC3 and HeLa) have been identified. The developed QSARs have the capacity to be implemented in advanced targeted delivery of liposomal drugs.

Development of a novel method for determination of mercury based on its inhibitory effect on horseradish peroxidase activity followed by monitoring the surface plasmon resonance peak of gold nanoparticles.

A highly sensitive and simple indirect spectrophotometric method has been developed for the determination of trace amounts of inorganic mercury (Hg(2+)) in aqueous media. The method is based on the inhibitory effect of Hg(2+) on the activity of horseradish peroxidase (HRP) in the oxidation of ascorbic acid by hydrogen peroxide followed by the reduction of Au(3+) to Au-NPs by unreacted ascorbic acid and the measurement of the absorbance of localized surface plasmon resonance (LSPR) peak of gold nanoparticles (at 530 nm) which is directly proportional to the concentration of Hg(2+). Under the optimum conditions, the calibration curve was linear in the concentration range of 1-220 ng mL(-1). Limits of detection (LOD) and quantification (LOQ) were 0.2 and 0.7 ng mL(-1), respectively and the relative standard deviation at 100 ng mL(-1) level of Hg(2+) was 2.6%. The method was successfully applied to the determination of mercury in different water samples.

Colourimetric-based method for the diagnosis of spinal muscular atrophy using gold nanoprobes.

Although numerous molecular methods for spinal muscular atrophy (SMA) detection have been exploited, most of them are laborious, time consuming and costly. Recently, gold nanoparticles (AuNPs) have attracted attention in the field of colourimetric bioanalysis, because AuNP aggregation can be tracked with the naked eye as well as ultraviolet-visible (UV-vis) peak analysis. Here, based on a non-cross linking platform, a colourimetric-based method was used to evaluate the capability of thiolated oligo-AuNPs (Au nanoprobes) to distinguish between normal individuals, carriers and those with SMA. In this platform, removal of the repulsive force of the Au nanoprobes using high salt concentration solutions forced them to aggregate. Amplified DNA products from 20 blood samples were hybridised with the Au nanoprobes. UV-vis spectra and peak analysis ratios of SMA-positive samples revealed that, following salt addition, the unhybridised Au nanoprobes progressively aggregated and their absorption peak shifted to longer wavelengths (P<0.05), observed as a colour change from red to violet-purple. In contrast, colourimetric discrimination between normal and carrier samples following salt addition was not possible because of the small differences in their spectra and aggregation indices. Using this method, patients can be screened in <30 min.

Protein corona composition of gold nanoparticles/nanorods affects amyloid beta fibrillation process.

Protein fibrillation process (e.g., from amyloid beta (Aß) and α-synuclein) is the main cause of several catastrophic neurodegenerative diseases such as Alzheimer's and Parkinson diseases. During the past few decades, nanoparticles (NPs) were recognized as one of the most promising tools for inhibiting the progress of the disease by controlling the fibrillation kinetic process; for instance, gold NPs have a strong capability to inhibit Aß fibrillations. It is now well understood that a layer of biomolecules would cover the surface of NPs (so called "protein corona") upon the interaction of NPs with protein sources. Due to the fact that the biological species (e.g., cells and amyloidal proteins) "see" the protein corona coated NPs rather than the pristine coated particles, one should monitor the fibrillation process of amyloidal proteins in the presence of corona coated NPs (and not pristine coated ones). Therefore, the previously obtained data on NPs effects on the fibrillation process should be modified to achieve a more reliable and predictable in vivo results. Herein, we probed the effects of various gold NPs (with different sizes and shapes) on the fibrillation process of Aß in the presence and absence of protein sources (i.e., serum and plasma). We found that the protein corona formed a shell at the surface of gold NPs, regardless of their size and shape, reducing the access of Aß to the gold inhibitory surface and, therefore, affecting the rate of Aß fibril formation. More specifically, the anti-fibrillation potencies of various corona coated gold NPs were strongly dependent on the protein source and their concentrations (10% serum/plasma (simulation of an in vitro milieu) and 100% serum/plasma (simulation of an in vivo milieu)).

Determination of nanoparticles using UV-Vis spectra.

Nanoparticles (NPs) are increasingly being used in different branches of science and in industrial applications; however, their rapid detection and characterization at low concentration levels have remained a challenge; more specifically, there is no single technique that can characterize the physicochemical properties of NPs (e.g. composition and size). In this work we have developed a colorimetric sensor array for defining the physicochemical properties of NPs in aqueous solution with ultra-low concentrations (e.g. 10(-7) g ml(-1) for gold NPs). Various NPs were readily identified using a standard chemometric approach (i.e. hierarchical clustering analysis), with no misclassifications over 400 trials.

Artificial neural network assisted kinetic spectrophotometric technique for simultaneous determination of paracetamol and p-aminophenol in pharmaceutical samples using localized surface plasmon resonance band of silver nanoparticles.

Spectrophotometric analysis method based on the combination of the principal component analysis (PCA) with the feed-forward neural network (FFNN) and the radial basis function network (RBFN) was proposed for the simultaneous determination of paracetamol (PAC) and p-aminophenol (PAP). This technique relies on the difference between the kinetic rates of the reactions between analytes and silver nitrate as the oxidizing agent in the presence of polyvinylpyrrolidone (PVP) which is the stabilizer. The reactions are monitored at the analytical wavelength of 420nm of the localized surface plasmon resonance (LSPR) band of the formed silver nanoparticles (Ag-NPs). Under the optimized conditions, the linear calibration graphs were obtained in the concentration range of 0.122-2.425µgmL(-1) for PAC and 0.021-5.245µgmL(-1) for PAP. The limit of detection in terms of standard approach (LODSA) and upper limit approach (LODULA) were calculated to be 0.027 and 0.032µgmL(-1) for PAC and 0.006 and 0.009µgmL(-1) for PAP. The important parameters were optimized for the artificial neural network (ANN) models. Statistical parameters indicated that the ability of the both methods is comparable. The proposed method was successfully applied to the simultaneous determination of PAC and PAP in pharmaceutical preparations.

Useful multivariate kinetic analysis: Size determination based on cystein-induced aggregation of gold nanoparticles.

This study describes spectrometric monitored kinetic processes to determine the size of citrate-capped Au nanoparticles (Au NPs) based on aggregation induced by l-cysteine (l-Cys) as a molecular linker. The Au NPs association process is thoroughly dependent on pH, concentration and size of nanoparticles. Size dependency of aggregation inspirits to determine the average diameters of Au NPs. For this aim the procedure is achieved in aqueous medium at pH 7 (phosphate buffer), and multivariate data including kinetic spectra of Au NPs are collected during aggregation process. Subsequently partial least squares (PLS) modeling is carried out analyzing the obtained data. The model is built on the basis of relation between the kinetics behavior of aggregation and different Au NPs sizes. Training the model was performed using latent variables (LVs) of the original data. The analytical performance of the model was characterized by relative standard error. The proposed method was applied to determination of size in unknown samples. The predicted sizes of unknown samples that obtained by the introduced method are interestingly in agreement with the sizes measured by Transmission Electron Microscopy (TEM) images and Dynamic Light Scattering (DLS) measurement.