Machine-learning assisted multicolor platform for multiplex detection of antibiotics in environmental water samples

Antibiotic (AB) resistance is one of daunting challenges of our time, attributed to overuse of ABs and usage of AB-contaminated food resources. Due to their detrimental impact on human health, development of visual detection methods for multiplex sensing of ABs is a top priority. In present study, a colorimetric sensor array consisting of two types of gold nanoparticles (AuNPs) were designed for identification and determination of ABs. Design principle of the probe was based on aggregation of AuNPs in the presence of ABs at different buffer conditions. The utilization of machine learning algorithms in this design enables classification and quantification of ABs in various samples. The response profile of the array was analyzed using linear discriminant analysis algorithm for classification of ABs. This colorimetric sensor array is capable of accurate distinguishing between individual ABs and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit demonstrated the potential applicability of the developed sensor array in multiplex detection of ABs. The response profiles of the array were linearly correlated to the concentrations of ABs in a wide range of concentration with limit of detections of 0.05, 0.03, 0.04, 0.01, 0.06, 0.05 and 0.04 μg.mL-1 for azithromycin, amoxicillin, ciprofloxacin, clindamycin, cefixime, doxycycline and metronidazole respectively. The practical applicability of this method was further investigated by analysis of mixture samples of ABs and determination of ABs in river and underground water with successful verification.

Chrono-colorimetric sensor array for detection and discrimination of halide ions using an all-in-one plasmonic sensor element

Most nanoparticle based colorimetric sensor array utilize several sensor elements and static response for discrimination of target analytes. This approach can be complicated and costly to synthesize or functionalize different nanoparticles for providing wide color variation. Herein, triangular silver nanoparticles (TSNPs) were used to develop a colorimetric sensor array by time-dimension responses. The principle of this sensor array is based on the diverse etching process of TSNPs in the presence of three halide ions, including bromide (Br^-), iodide (I^-) and chloride (Cl^-). Various etchings of TSNPs induced color changes at different reaction time intervals, which produced a colorimetric pattern for each ion. Therefore, using time dependent etching responses of TSNPs as a single sensing component can produce a wide color variation which can be distinguished by naked eyes. The colorimetric responses of TSNPs upon the addition of different concentrations of halide ions have been analyzed by PLS regression (PLS-R) and PLS discriminant analysis (PLS-DA). The analytical figures of merit confirmed that the developed chrono-colorimetric TSNPs -based sensor array is successful in both the discrimination and quantitative detection of halide ions. At the final step, the three halide ions were accurately determined in a real water sample, which verified the potential of the developed sensor in a real sample.

Machine-learning assisted multiplex detection of catecholamine neurotransmitters with a colorimetric sensor array

Catecholamine neurotransmitters (CNs), such as dopamine (DA), epinephrine (EP), norepinephrine (NEP), and levodopa (LD), are recognized as the primary biomarkers of a variety of neurological illnesses. Therefore, simultaneous monitoring of these biomarkers is highly recommended for clinical diagnosis and treatment. In this study, a high-performance colorimetric artificial tongue has been proposed for the multiplex detection of CNs. Different aggregation behaviors of gold nanoparticles in the presence of CNs under various buffering conditions generate unique fingerprint response patterns. Under various buffering conditions, the distinct acidity constants of CNs, and consequently their predominant species at a given pH, drive the aggregation of gold nanoparticles (AuNPs). The utilization of machine learning algorithms in this design enables classification and quantification of CNs in various samples. The response profile of the array was analyzed using the linear discriminant analysis algorithm for classification of CNs. This colorimetric sensor array is capable of accurately distinguishing between individual neurotransmitters and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit (FOMs) and linear ranges of 0.6-9 μM (R² = 0.99) for DA, 0.1-10 μM (R² = 0.99) for EP, 0.1-9 μM (R² = 0.99) for NEP and 1-70 μM (R² = 0.99) for LD demonstrated the potential applicability of the developed sensor array in precise and accurate determination of CNs. Finally, the feasibility of the array was validated in human urine samples as a complex biological fluid with LODs of 0.3, 0.5, 0.2, and 1.9 μM for DA, EP, NEP, and LD, respectively.

Radial basis function-artificial neural network (RBF-ANN) for simultaneous fluorescent determination of cysteine enantiomers in mixtures

The determination of chiral compounds is critically important in chemical and pharmaceutical sciences. Cysteine amino acid is one of the important chiral compounds where each enantiomer (L and D) has different effects on fundamental physiological processes. The unique optical properties of nanoparticles make them a suitable probe for the determination of different analytes. In this work, the water-soluble thioglycolic acid (TGA)-capped cadmium-telluride (CdTe) quantum dots (QDs) were applied as optical nanoprobe for the simultaneous determination of cysteine enantiomers. The difference in the kinetics of the interactions between L- and D-cysteine with CdTe QDs is used for multivariate quantitative analysis. Multivariate methods are superior to univariate methods in determining the concentration of each enantiomer in the mixture without the information about the total chiral analyte concentration. As a nonlinear calibration method the radial basis function -artificial neural network (RBF-ANN) model was more successful in predicting L-and D-cysteine concentrations than the linear partial least squares regression (PLS) model.

Simultaneous detection and identification of thiometon, phosalone, and prothioconazole pesticides using a nanoplasmonic sensor array

In this work, a colorimetric sensor array has been designed for the identification and discrimination of thiometon (TM) and phosalone (PS) as organophosphate pesticides and prothioconazole (PC) as a triazole pesticide. For this purpose, two different plasmonic nanoparticles including unmodified gold nanoparticles (AuNPs) and unmodified silver nanoparticles (AgNPs) were used as sensing elements. The principle of the proposed strategy relied on the aggregation AuNPs and AgNPs through the cross-reactive interaction between the target pesticides and plasmonic nanoparticles. Therefore, these aggregation-induced UV-Vis spectra changes were utilized to discriminate the target pesticides with the help of linear discriminant analysis (LDA). Besides, we have employed the bar plots and the heat maps as visual non-statistical methods to differentiate the pesticides in a wide range of concentrations (i.e., 20-5000 ng mL^-1). Multivariate calibration plots from partial least squares (PLS)- regression indicated that the responses linearly depend on the pesticide concentrations in the range of 100-1000 ng mL^-1 with the limit of detections (LOD) of 66.8, 68.3, and 41.4 ng mL^-1, for TM, PS, and PC, respectively. Finally, the potential applicability of the proposed sensor array has been evaluated for the detection and identification of the pesticides in the mixtures, water samples, and cucumber fruit.

Protective Effects of Gold Nanoparticles Against Malathion-Induced Cytotoxicity in Caco-2 Cells

Malathion is an organophosphorus insecticide widely used in agriculture, residential area, and public health programs with a known mechanism of toxicity of inhibition of acetylcholinesterase and induction of oxidative stress. Gold nanoparticles (AuNPs) represent stable and easily synthesized nanoparticles with extensive use in consumer products and medicine. Due to the antioxidant property of AuNPs, it is possible that AuNPs may prevent malathion-induced oxidative damage. In this study, the cytotoxicity of malathion and AuNPs (10 and 20 nm) were measured separately in Caco-2 cells. Then the protective effects of AuNPs were evaluated by measuring the oxidative stress (lipid peroxidation level and glutathione content) and acetylcholinesterase activity. The calculated IC50s values at 48 hr were 326.8±0.32, 43.09±0.65, and 41.46±0.24 µg/ml for malathion, AuNPs 10 and 20 nm, respectively. Then, the lowest concentration of AuNPs (1 µg/ml) and IC50 concentration of malathion (326.8 µg/ml) were selected to evaluate the effects of pretreatment of Caco-2 cells with AuNPs before exposure to malathion were evaluated. Interestingly, the results showed remarkably significant protective effects of AuNPs by attenuation the different parameters of oxidative stress and cytotoxicity induced by malathion in cells (P<0.001). It is the first report showing the protective effects of AuNPs against malathion-induced cytotoxicity in the Caco-2 cell line.

Nanoplasmonic sensor array for the detection and discrimination of pesticide residues in citrus fruits

Great attention has been directed towards developing rapid and straightforward methods for the identification of various pesticides that are usually used simultaneously in citrus fruits. The extensive use of diverse classes of pesticides in citrus fruits and their high toxicity may cause serious diseases in the human body. In the current study, a non-enzymatic sensor array has been developed for the identification and discrimination of five different pesticides belonging to diverse classes, including organophosphate, carbamate, and bipyridylium. For this aim, two gold nanoparticles (AuNPs) with different capping agents, citrate and borohydride, were used as sensing elements. The aggregation-induced spectra alterations of AuNPs were utilized to identify the pesticides in a wide range of concentrations (20-5000 ng mL-1). We have employed data visualization methods (i.e., heat maps, bar plots, and color difference maps), a supervised pattern recognition method (i.e., linear discrimination analysis), and partial least squares regression to qualitatively and quantitatively determine the pesticides. Finally, the practical applicability of the developed sensor array was evaluated for the identification of target pesticides in lime peel. The outcomes revealed that the probe could accurately verify the absence or presence of the pesticides in lime fruit.

Selective colorimetric detection of pentaerythritol tetranitrate (PETN) using arginine-mediated aggregation of gold nanoparticles

Detection of pentaerythritol tetranitrate (PETN) as an explosive has been of great interest because of public safety and military concerns. Here, we have presented a simple, selective and sensitive colorimetric method for direct detection of PETN. The gold nanoparticles (AuNPs) were first exposed to arginine which has primary amines in its structure. Electron deficient -NH₂ groups from arginine could strongly interact with -NO₂ groups of PETN as electron donors. Hydrogen bonding happens between the -NO₂ group of PETN and -NH₂ group of arginine molecules. Therefore, selective aggregation of AuNPs happened because of the donor-acceptor and hydrogen bonding interactions. Due to the aggregation, the color of reddish AuNPs turned to blue or purple depend on PETN concentration. A good linear relationship was achieved between the aggregation signal (absorbance ratio of A₆₅₀/A₅₂₀) of the probe and the concentration of PETN with a limit of detection of 0.169 μmol L^-1. Furthermore, we have found that the developed probe can detect PETN in complex matrices of groundwater and soil samples.

Identification of Catecholamine Neurotransmitters Using a Fluorescent Electronic Tongue

Catecholamine neurotransmitters, specifically, dopamine (DA), epinephrine (EP), and norepinephrine (NE), are known as substantial indicators of various neurological diseases. Developing rapid detection methods capable of simultaneously screening their concentrations is highly desired for early clinical diagnosis of such diseases. To this aim, we have designed an optical sensor array using three fluorescent dyes with distinct emission bands and have monitored variations in their emission profiles upon the addition of DA, EP, and NE in the presence of gold ions. Because of the different reducing power of catecholamines, differently sized gold nanoparticles (GNPs) with different levels of aggregation were generated, resulting in different amounts of spectral overlap between the absorption band of the in situ generated plasmonic GNPs and the emission bands of the fluorescent dyes. These energy-transfer-based fingerprint profiles were used to discriminate the neurotransmitters by applying pattern recognition methods including linear discriminant analysis (LDA) and artificial neural networks (ANN) and to determine their concentration using multiple linear regression (MLR). Our proposed array also showed a good performance in the discrimination of DA, EP, and NE in complex biological media such as human urine.

Optical nanoprobes for chiral discrimination

Chiral recognition can be achieved by exploiting chiral properties of nanoparticles within various colorimetric and luminescent sensing systems.

ThThnated Development of a pH assisted AgNP-based colorimetric sensor Array for simultaneous identification of phosalone and azinphosmethyl pesticides

Development of simple and rapid methods for identification of pesticides, due to their broad usage and harmful effects on mammals, has been known as a critical demand. Herein, we have introduced a silver nanoparticle (AgNP) based colorimetric sensor array for simultaneous identification of Azinphosmethyl (AM) and Phosalone (PS) pesticides. In the presence of the target pesticides, unmodified AgNPs at various pHs (4.5, 5.5 and 9.5) showed different aggregation behaviors. As a result of aggregation, the color and UV-Vis spectra of AgNPs changed differentially, leading to distinct response patterns for AM and PS. The aggregation induced spectral changes of AgNPs, were used to identify AM and PS with the help of linear discriminant analysis (LDA). The applicability of the proposed sensor array was then evaluated by identifying the target pesticides in apple samples. Altogether, the developed AgNPs based colorimetric sensor array can be potentially exploited as an efficient discrimination tool in the near future for agrichemical applications.

Ratiometric fluorescent nanoprobes for visual detection: Design principles and recent advances - A review

Signal generation techniques for visual detection of analytes have received a great deal of attention in various sensing fields. These approaches are considered to be advantageous when instrumentation cannot be employed, such as for on-site assays, point-of-care tests, and he althcare diagnostics in resource-constrained areas. Amongst various visual detection approaches explored for non-invasive quantitative measurements, ratiometric fluorescence sensing has received particular attention as a potential method to overcome the limitations of intensity-based probes. This technique relies on changes in the intensity of two or more emission bands (induced by an analyte), resulting in an effective internal referencing which improves the sensitivity of the detection. The self-calibration, together with the unique optophysical properties of nanoparticles (NPs) have made the ratiometric fluorescent nanoprobes more sensitive and reliable, which in turn, can result in more precise visual detection of the analytes. Over the past few years, a vast number of ratiometric sensing probes using nanostructured fluorophores have been designed and reported for a wide variety of sensing, imaging, and biomedical applications. In this work, a review on the NP-based ratiometric fluorescent sensors has been presented to meticulously elucidate their development, advances and challenges. With a special emphasis on visual detection, the most important steps in the design of fluorescent ratiometric nanoprobes have been given and based on different classes of analytes, recent applications of fluorescent ratiometric nanoprobes have been summarized. The challenges for the future use of the technique investigated in this review have been also discussed.

Nanoparticle-based Chemiluminescence for Chiral Discrimination of Thiol-Containing Amino Acids

The ability to recognize the molecular chirality of enantiomers is extremely important owing to their critical role in drug development and biochemistry. Convenient discrimination of enantiomers has remained a challenge due to lack of unsophisticated methods. In this work, we have reported a simple strategy for chiral recognition of thiol-containing amino acids including penicillamine (PA), and cysteine (Cys). We have successfully designed a nanoparticle-based chemiluminescence (CL) system based on the reaction between cadmium telluride quantum dots (CdTe QDs) and the enantiomers. The different interactions of CdTe QDs with PA enantiomers or Cys enantiomers led to different CL intensities, resulting in the chiral recognition of these enantiomers. The developed method showed the ability for determination of enantiomeric excess of PA and Cys. It has also obtained an enantioselective concentration range from 1.15 to 9.2 mM for PA. To demonstrate the potential application of this method, the designed platform was applied for the quantification of PA in urine and tablet samples. For the first time, we presented a novel practical application of nanoparticle-based CL system for chiral discrimination.

Cell shape affects nanoparticle uptake and toxicity: An overlooked factor at the nanobio interfaces

HYPOTHESIS

It is now being increasingly accepted that cells in their native tissue show different morphologies than those grown on a culture plate. Culturing cells on the conventional two-dimensional (2D) culture plates does not closely resemble the in vivo three-dimensional (3D) structure of cells which in turn seems to affect cellular function. This is one of the reasons, among many others, that nanoparticles uptake and toxicology data from 2D culture plates and in vivo environments are not correlated with one another. In this study, we offer a novel platform technology for producing more in vivo-like models of in vitro cell culture.

EXPERIMENTS

The normal fibroblast cells (HU02) were cultured on "pseudo-3D" substrates, made from cell imprinting approach. The respond of the cells to a model nanoparticle (gold nanorod) were compared in 2D and "pseudo-3D" cultures modes, by cytotoxicological assays.

FINDINGS

It is illustrated here that the cells' respond to the exact same type of nanoparticles is majorly dependant in their shape. The use of "pseudo-3D" substrates which could partially mimic the shape of cells in vivo is strongly proposed as a means of better predicting the efficacy of the 2D cell culture plates.

Label-free detection of β-amyloid peptides (Aβ40 and Aβ42): a colorimetric sensor array for plasma monitoring of Alzheimer's disease

Monitoring the ratio of 40- and 42-residue amyloid β peptides (i.e., Aβ40 and Aβ42) in human plasma is considered one of the hallmarks of detection of the early stage of Alzheimer's disease (AD). Therefore, development of a specific, yet non-antibody-based method for simultaneous detection of Aβ40 and Aβ42 may have considerable clinical applications. Here, we developed a 'nanoparticle-based colorimetric sensor array' utilizing label-free gold and silver nanoparticles for visual detection of Aβ42 and Aβ40. Different aggregation behaviors of nanoparticles through their conjugation with Aβ42 and Aβ40 followed by the coordination of Aβ42 and Aβ40 with Cu(ii) led to diverse spectral and color changes. The spectral changes were quantitatively differentiated by a supervised pattern recognition approach, linear discriminant analysis (LDA). The proposed sensor array was able to discriminate among Aβ42, Aβ40, and HSA in different concentrations (50 nmol L-1 to 500 nmol L-1) and their mixtures. Moreover, the sensor array had the capability to identify structurally similar Aβ peptides in human plasma samples. The developed sensor array technology might pave the way for a cheap and rapid, yet robust, platform for high-throughput screening of human plasma for defining the at-risk population for AD.

Gold Nanorod-Based Chrono-Colorimetric Sensor Arrays: A Promising Platform for Chemical Discrimination Applications

Most array-based sensing platforms, to date, utilize static response patterns for discrimination of a wide variety of analytes, but only a few studies have focused on the important task of quantitatively resolving structural isomers, which are nowadays important because of their broad usage in medicines and industries. A possible way of accomplishing this feat is to combine kinetic (rather than static) sensor response profiles with the chemical tongue strategy to allow the development of array-based sensors for isomeric discrimination. Here, by adding the time dimension, a simple and novel gold nanorod (AuNR)-based chrono-colorimetric sensor array is proposed for chemical discrimination applications. Because of their similar structure but different redox potentials, dihydroxybenzene (DHB) structural isomers have been chosen, as models, to evaluate the applicability of the proposed array. The principle of the array relies on various growth rates of silver shells on AuNRs at different silver ion/AuNR concentration ratios owing to the different kinetic behaviors of DHBs, which can be used as fingerprints to identify DHBs with the help of multivariate analysis methods. The combinatorial colorimetric response of AuNRs upon DHB addition has been analyzed by linear discriminant analysis and hierarchical cluster analysis. Finally, identification of individual DHBs or their mixtures in real samples confirms the potential application of the proposed array.

A nanopaper-based artificial tongue: a ratiometric fluorescent sensor array on bacterial nanocellulose for chemical discrimination applications

In the present study, a ratiometric fluorescent sensor array as an artificial tongue has been developed on a nanopaper platform for chemical discrimination applications. The bacterial cellulose (BC) nanopaper was utilized for the first time as a novel, flexible, and transparent substrate in the optical sensor arrays for developing high-performance artificial tongues. To fabricate this platform, the hydrophobic walls on the BC nanopaper substrates were successfully created using a laser printing technology. In addition, we have used the interesting photoluminescence (PL) properties of an immobilized ratiometric probe (carbon dot-Rhodamine B (CD-RhB) nanohybrids) on the nanopaper platform to improve the visual discrimination analysis. Heavy metal ions were utilized as model analytes to verify the applicability of the fabricated nanopaper-based ratiometric fluorescent sensor array (NRFSA). Using the color variation of the NRFSA platform upon the addition of heavy metal ions, which have been obtained by a smartphone (under an UV irradiation), five heavy metal ions (i.e., Hg(ii), Pb(ii), Cd(ii), Fe(iii), and Cu(ii)) have been well-distinguished through the RGB analysis via production of the characteristic PL fingerprint-like response patterns for each of them. Moreover, the developed optical sensor array was successfully exploited to identify the heavy metal ions in the water and fish samples. We have also found that the PL spectra, which have been obtained by a spectrofluorometer, of the developed NRFSA can be exploited for discrimination applications. We believe that the nanopaper-based artificial tongues will provide innovative insights into the development of optical sensor arrays towards advanced (bio)chemical discrimination applications and can revolutionize the conventional optical sensor array technology.

A rainbow ratiometric fluorescent sensor array on bacterial nanocellulose for visual discrimination of biothiols

The crucial role of biothiols in many biological processes, which turns them into important biomarkers for the early diagnosis of various diseases, the development of an affordable, sensitive and portable probe for the detection and discrimination of these compounds is of great importance.

Aggregation-based colorimetric sensor for determination of prothioconazole fungicide using colloidal silver nanoparticles (AgNPs)

There is a growing interest in developing high-performance sensors monitoring fungicides, due to their broadly usage and their adverse effects on humans and wildlife. In the present study, a colorimetric probe has been proposed for detection of prothioconazole based on aggregation of unmodified silver nanoparticles (AgNPs). Under optimized condition, linear relationships between the concentration of prothioconazole and the absorbance ratio of A₅₀₀/A₃₉₅ were found over the range of 0.01μg·mL^-1 to 0.4μg·mL^-1 with quantification limit as low as 1.7ng·mL^-1. Furthermore, AgNPs color change from yellow to pink-orange in presence of prothioconazole, indicates highly sensitive naked-eye colorimetric assay for quantifying prothioconazole in real applications. The proposed approach was successfully used for the determination of prothioconazole in wheat flour and paddy water sample.

Nanoparticle-based optical sensor arrays

As in many other methods that have integrated nanoparticles (NPs), the chemical nose/tongue strategy has also progressed greatly since the entrance of NPs into this field. The fascinating tunable physicochemical properties of NPs have made them powerful candidates for array-based sensing platforms and have enabled the development of real-time, sensitive and portable systems that are able to target complex mixtures of analytes. In particular, the unique optical properties of NPs have a key role in providing promising array-based sensing approaches. This review will describe the main aspects and processes of most common NP-based optical sensor arrays. The fundamental steps in the design of a sensor array together with details of each step would be provided. The review begins with the principles of optical sensor arrays and presents the concept of cross-reactivity as the main criterion in the selection of sensing elements. Changes in the absorption and emission properties of the assembled sensing elements are categorized into two main classes of optical signals (colorimetric and fluorometric). Popular chemometric methods used for analyzing the data acquired by a sensor array have also been briefly introduced. On the basis of the objective and the desired application, different types of plasmonic and fluorescent NP that possess unique opto-physical properties have been presented as available choices in the design of sensing elements. The vast number of applications of NP-based optical sensor arrays published throughout the literature have then been reviewed according to their mechanism of interaction and the type of optical signal. Finally, the remaining challenges and future directions in this topic have been highlighted.

Colorimetric Fingerprints of Gold Nanorods for Discriminating Catecholamine Neurotransmitters in Urine Samples

Catecholamine neurotransmitters, generally including dopamine (DA), epinephrine (EP) and norepinephrine (NE) are known as substantial indicators of various neurological diseases. Simultaneous detection of these compounds and their metabolites is highly recommended in early clinical diagnosis. To this aim, in the present contribution, a high performance colorimetric sensor array has been proposed for the detection and discrimination of catecholamines based on their reducing ability to deposit silver on the surface of gold nanorods (AuNRs). The amassed silver nanoshell led to a blue shift in the longitudinal localized surface plasmon resonance (LSPR) peak of AuNRs, creating a unique pattern for each of the neurotransmitters. Hierarchical cluster analysis (HCA) and linear discriminate analysis (LDA) pattern recognition techniques were employed to identify DA, EP and NE. The proposed colorimetric array is able to differentiate among individual neurotransmitters as well as their mixtures, successfully. Finally, it was shown that the sensor array can identify these neurotransmitters in human urine samples.

Time-Resolved Visual Chiral Discrimination of Cysteine Using Unmodified CdTe Quantum Dots

Herein, we demonstrate a simple yet novel luminescence assay for visual chiral discrimination of cysteine. Thioglycolic acid (TGA)-capped cadmium-telluride (CdTe) quantum dots (QDs) exposing green emission were directly synthesized in aqueous solution. The interaction between cysteine molecules and CdTe QDs induced the aggregation of QDs via hydrogen bonding. As a result of electronic coupling within these aggregates, a redshift both in the absorption and emission spectra of QDs occured. The difference in the kinetics of the interactions between L- and D-cysteine with CdTe QDs led to chiral recognition of these enantiomers. Addition of D-cysteine to CdTe QDs in a basic media caused a green-to-yellow color change, while no color alteration in QDs emission was observed in the presence of L-cysteine after 2 hours. Notably, the QDs used in the proposed assay are free from any labling/modification, which makes the present strategy highly attractive for sensing applications. Furthermore, the presented chiral assay is able to determine the enantiomeric excess (ee) of D-cysteine in the whole range of ee values (from -100% to 100%).

Design a New Strategy Based on Nanoparticle-Enhanced Chemiluminescence Sensor Array for Biothiols Discrimination

Array-based sensor is an interesting approach that suggests an alternative to expensive analytical methods. In this work, we introduce a novel, simple, and sensitive nanoparticle-based chemiluminescence (CL) sensor array for discrimination of biothiols (e.g., cysteine, glutathione and glutathione disulfide). The proposed CL sensor array is based on the CL efficiencies of four types of enhanced nanoparticle-based CL systems. The intensity of CL was altered to varying degrees upon interaction with biothiols, producing unique CL response patterns. These distinct CL response patterns were collected as “fingerprints” and were then identified through chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). The developed array was able to successfully differentiate between cysteine, glutathione and glutathione disulfide in a wide concentration range. Moreover, it was applied to distinguish among the above analytes in human plasma.

A new bifunctional hybrid nanostructure as an active platform for photothermal therapy and MR imaging

As a bi-functional cancer treatment agent, a new hybrid nanostructure is presented which can be used for photothermal therapy by exposure to one order of magnitude lower laser powers compared to similar nanostructures in addition to substantial enhancment in magnetic resonance imaging (MRI) contrast. This gold-iron oxide hybrid nanostructure (GIHN) is synthesized by a cost-effective and high yield water-based approach. The GIHN is sheilded by PEG. Therefore, it shows high hemo and biocompatibility and more than six month stability. Alongside earlier nanostructures, the heat generation rate of GIHN is compareable with surfactnat-capped gold nanorods (GNRs). Two reasons are behind this enhancement: Firstly the distance between GNRs and SPIONs is adjusted in a way that the surface plasmon resonance of the new nanostructure is similar to bare GNRs and secondly the fraction of GNRs is raised in the hybrid nanostructure. GIHN is then applied as a photothermal agent using laser irradiation with power as low as 0.5 W.cm(-2) and only 32% of human breast adenocarcinoma cells could survive. The GIHN also acts as a dose-dependent transvers relaxation time (T2) MRI contrast agent. The results show that the GINH can be considered as a good candidate for multimodal photothermal therapy and MRI.

Multi-response optimization followed by multivariate calibration for simultaneous determination of carcinogenic polycyclic aromatic hydrocarbons in environmental samples using gold nanoparticles

A multivariate-based strategy was developed for simultaneous determination of thirteen carcinogenic PAHs in water samples using AuNPs.

Colorimetric detection of glutathione based on transverse overgrowth of high aspect ratio gold nanorods investigated by MCR-ALS

In this paper, we present a simple platform for colorimetric detection of glutathione using gold nanorods (AR ∼ 6.5 ± 0.2) as a plasmonic sensor.

A sensitive and selective colorimetric method for detection of copper ions based on anti-aggregation of unmodified gold nanoparticles

A highly sensitive and selective colorimetric method for detection of copper ions, based on anti-aggregation of D-penicillamine (D-PC) induced aggregated gold nanoparticles (AuNPs) was developed. Copper ions can hinder the aggregation of AuNPs induced by D-PC, through formation of mixed-valence complex with D-PC that is a selective copper chelator. In the presence of a fixed amount of D-PC, the aggregation of AuNPs decreases with increasing concentrations of Cu(2+) along with a color change from blue to red in AuNPs solution and an increase in the absorption ratio (A520/A650). Under the optimum experimental conditions (pH 7, [AuNPs] =3.0 nmol L(-1) and [NaCl]=25 mmol L(-1)), a linear calibration curve for Cu(2+) was obtained within the range of 0.05-1.85 µmol L(-1) with a limit of detection (3Sb) of 30 nmol L(-1). Excellent selectivity toward Cu(2+) was observed among various metal ions due to a specific complex formation between Cu(2+) and D-PC. The proposed method has been successfully applied for the detection of Cu(2+) in various real samples.

Assembly of CeO2-TiO2 nanoparticles prepared in room temperature ionic liquid on graphene nanosheets for photocatalytic degradation of pollutants

CeO(2)-TiO(2) nanoparticles were prepared by the sol-gel process using 2-hydroxylethylammonium formate as room-temperature ionic liquid and calcined at different temperatures (500-700°C). CeO(2)-TiO(2)-graphene nanocomposites were prepared by hydrothermal reaction of graphene oxide with CeO(2)-TiO(2) nanoparticles in aqueous solution of ethanol. The photocatalysts were characterized by X-ray diffraction, BET surface area, diffuse reflectance spectroscopy, scanning electron microscopy, and Fourier transformed infrared techniques. The results demonstrate that the room-temperature ionic liquid inhibits the anatase-rutile phase transformation. This effect was promoted by addition of CeO(2) to TiO(2). The addition of graphene to CeO(2)-TiO(2) nanoparticles enhances electron transport and therefore impedes the charge recombination of excited TiO(2). The photodegradation results of the pollutants in aqueous medium under UV irradiation revealed that CeO(2)-TiO(2)-graphene nanocomposites exhibit much higher photocatalytic activity than CeO(2)-TiO(2) and pure TiO(2). The photocatalytic activity of CeO(2)-TiO(2)-graphene nanocomposites decreases with additional increasing of the graphene content. Moreover, comparison of the photocatalytic activities of CeO(2)-TiO(2)-graphene with the other CeO(2)-TiO(2)-carbon demonstrates that CeO(2)-TiO(2)-graphene nanocomposites have the highest photocatalytic activity due to their unique structure and electronic properties. Chemical oxygen demand for solutions of the pollutants gave a good idea about mineralization of them.

Application of silver nanoparticles and principal component-artificial neural network models for simultaneous determination of levodopa and benserazide hydrochloride by a kinetic spectrophotometric method

A multicomponent analysis method based on principal component analysis-artificial neural network model (PC-ANN) is proposed for the simultaneous determination of levodopa (LD) and benserazide hydrochloride (BH). The method is based on the reaction of levodopa and benserazide hydrochloride with silver nitrate as an oxidizing agent in the presence of PVP and formation of silver nanoparticles. The reaction monitored at analytical wavelength 440 nm related to surface plasmon resonance band of silver nanoparticles. Differences in the kinetic behavior of the levodopa and benserazide hydrochloride were exploited by using principal component analysis, an artificial neural network (PC-ANN) to resolve concentration of analytes in their mixture. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous determination of analytes in mixtures with relative standard errors of prediction in the region of 4.5 and 6.3 for levodopa and benserazide hydrochloride respectively. The results show that this method is an efficient method for prediction of these analytes.