Etched-suppressed gold nanorods providing highly distinctive plasmonic patterns: Towards multiplex analysis of neuroblastoma biomarkers.

BACKGROUND: On-site monitoring of vanillylmandelic acid (VMA), homovanillic acid (HVA), and dopamine (DA) as key diagnostic biomarkers for a wide range of neurological disorders holds utmost significance in clinical settings. Numerous colorimetric sensors with mechanistic approaches based on aggregation or silver metallization have been introduced for this purpose. However, these mechanisms have drawbacks, such as sensitivity to environmental factors and probe toxicity. Therefore, there is a great demand for a robust yet non-toxic colorimetric sensor that employs a novel route to monitor these biomarkers effectively. RESULTS: Here, we present a single-component multi-colorimetric probe based on the controllable etching suppression of gold nanorods (AuNRs) upon exposure to the mild etchant N-bromosuccinimide (NBS), designed to accurately detect and discriminate VMA, HVA, DA, and their corresponding mixtures, i.e. , VMA: HVA, VMA:DA, HVA:DA, and VMA:HVA:DA. To enhance the sensitivity and automation capabilities of the designed multi-colorimetric sensor, two machine learning techniques were employed: linear discriminant analysis (LDA) for the qualitative classification and partial least-squares regression (PLSR) for the quantitative analysis of pure biomarkers and their mixtures. The outcomes revealed a high correlation between measured and predicted values, covering a linear range of 0.8-25, 1.2-25, and 2.7-100 µmol L-1, with remarkably low detection limits of 0.260, 0.397, and 0.913 µmol L-1 for VMA, HVA, and DA, respectively. Lastly, the performance of the probe was validated by successfully detecting the neuroblastoma biomarker VMA:HVA in human urine. SIGNIFICANCE: Our designed multi-colorimetric probe introduces a rapid, cost-effective, user-friendly, non-toxic, and non-invasive approach to detecting and discriminating not only the pure biomarkers but also their corresponding binary and ternary mixtures. The distinctive response profiles produced by the probe in the presence of different mixture ratios can indicate various disease states in patients, which is highly crucial in clinical diagnostics.

Gold Nanorod Amalgamation: Machine Learning Empowered Discrimination of Biothiol and Thiol Ratios.

Biothiols, characterized by thiol groups, exhibit remarkable affinity for certain metals, playing pivotal roles in intracellular and extracellular biological processes. Fluctuations in their levels profoundly impact overall physiological health. Despite the development of various probes for biothiol detection and quantification, their inability to monitor thiol-to-disulfide state transitions persists as a limitation. Given their association with pathologies, early detection remains imperative. Gold nanorod (AuNR)-based colorimetric probes have garnered attention for their utility in visual diagnostic assays. Herein, we present a cost-effective, and sensitive multicolor ratio measuring probe enabling on-site simultaneous identification, discrimination, and quantification of essential biothiols─cysteine (CYS), glutathione (GSH), cystine (CYSS), and glutathione disulfide (GSSG)─while also quantifying thiol-to-disulfide ratios. Our investigation clarifies the probe's functionality, elucidating etching and antietching mechanisms based on sulfhydryl group coordination with Hg2+. This coordination impedes gold amalgam formation, facilitating discriminative detection via AuNR size and aspect ratio modulation, validated by transmission electron microscopy. Notably, distinct rainbow-like fingerprint patterns were discernible both visually and spectroscopically for the aforementioned biothiols and their respective thiol-to-disulfide ratios. Subsequent qualitative and quantitative analyses via linear discriminant analysis (LDA) and partial least squares regression revealed linear correlations over broad concentration ranges (CYS: 1.9-40 µmol L-1, GSH: 3.2-200.0 µmol L-1, CYSS: 2.0-70.0 µmol L-1, GSSG: 3.7-100.0 µmol L-1), with detection limits of 0.66 µmol L-1 (CYS), 1.07 µmol L-1 (GSH), 0.69 µmol L-1 (CYSS), and 1.24 µmol L-1 (GSSG). Moreover, thiol-to-disulfide ratios exhibited linear patterns within 0.2-5 µmol L-1, with detection limits of 0.13 and 0.09 µmol L-1, and exceptional analytical sensitivities of 32.648 and 49.782 for (CYS/CYSS) and (GSH/GSSG), respectively. Lastly, we evaluated the probe's performance in complex matrices relative to aqueous media, both quantitatively and qualitatively.

Multicolorimetric Sensor Array Based on Silver Metallization of Gold Nanorods for Discriminating Dopaminergic Agents.

Dopaminergic agents are compounds that modulate dopamine-related activity in the brain and peripheral nerves within the pathways on both sides of the blood-brain barrier. Atypical levels of them can precipitate a multitude of neurological disorders, whose timely diagnosis signifies not only stopping the advancement of the illness but also surmounting it. A silver metallized gold nanorod (AuNRs) conditional sensor array, designed to detect dopaminergic agents for assessing nervous system disorders, yielded significant results in simultaneous detection and discrimination of Benserazide (Benz), Levodopa (L-DOPA), and Carbidopa (Carb). The array was composed of two different concentrations of silver ions as sensor elements (SEs), which generated unique signatures indicative of the presence of reductive target analytes, triggered by the incongruent formation of the Au@Ag core-shell, causing visual and fingerprint colorimetric patterns. Generating diverse responses is the key to the functionality of array-based sensing, which facilitated achieving spectral and color variation originating from the blue shift of AuNRs longitudinal localized surface plasmon resonance (LLSPR) in the extinction spectrum. Also, employing a smartphone camera enables clear visual discrimination across an extensive concentration span. Pattern recognition through linear discriminant analysis (LDA) underscored the robust discrimination accuracies of this sensor, along with quantification by means of partial least-squares regression (PLSR), affirming its potential for practical applications. Notably, the array demonstrated high sensitivity in detecting varied concentrations of target analytes, even in commercial drug samples. The sensor responses exhibited a linear correlation with the concentrations of Benz, L-DOPA, and Carb ranging from 1.59 to 100.0, 5.26 to 100.0, and 5.32 to 100.0 µmol L-1, respectively, and the minimum detectable concentrations for Benz, L-DOPA, and Carb were measured at 0.53, 1.75, and 1.77 µmol L-1, respectively. The implemented machine-learning-empowered array-based sensor represents advancements in dopaminergic agent tracing and naked eye detection.

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.

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.

Optical nanoprobes for chiral discrimination.

Chiral discrimination has always been a hot topic in chemical, food and pharmaceutical industries, especially when dealing with chiral drugs. Enantiomeric recognition not only leads to better understanding of the mechanism of molecular recognition in biological systems, but may further assist in developing useful molecular devices in biochemical and pharmaceutical studies. By emerging nanotechnology and exploiting nanomaterials in sensing applications, a great deal of attention has been given to the design of optical nanoprobes that are able to discriminate enantiomers of chiral analytes. This review explains how engineering nanoparticles (NPs) with desired physicochemical properties allows developing novel optical nanoprobes for chiral recognition. Fundamental concepts related to the origin of chirality in NPs have been briefly presented. Colorimetric and fluorimetric assays in which different types of chiral NPs are used for enantioselective recognition, have been comprehensively described. The main types of nanomaterials described in this review consist of luminescent quantum dots (QDs), carbon dots (CDs), silicon NPs and metal nanoclusters (NCs), as well as plasmonic nanostructures. The mechanisms of sensing in these NP-based optical chiral assays along with relevant examples have been also discussed. Finally, the remaining challenges and future directions have been provided for researchers interested in this topic.