A colorimetric sensor array for detection and discrimination of biothiols based on aggregation of gold nanoparticles

Investigation of interactions between biological thiols and gold nanoparticles by capillary electrophoresis with laser-induced fluorescence

Biological thiols spontaneously form a stable Au-S dative bond with gold nanoparticles (AuNP) that might be used for their selective extraction and enrichment in biological samples. In this work, interactions of selected biological thiols (glutathione, cysteine, homocysteine [Hcys], cysteamine [CA], and N-acetylcysteine) with AuNP stabilized by different capping agents (citrate, Tween 20, Brij 35, CTAB, SDS) were investigated by UV-Vis spectroscopy and capillary electrophoresis with laser-induced fluorescence. Spectrophotometric measurements showed aggregation of Hcys and CA with AuNP. In contrast, it was confirmed by CE-LIF that biological thiols were adsorbed to all types of AuNP. Citrate-capped AuNP were selected for AuNP-based extraction of biological thiols from exhaled breath condensate (EBC). Dithiothreitol was utilized for desorption of biological thiols from the AuNP surface, which was followed by derivatization with eosin-5-maleimide and CE-LIF analysis. AuNP-based extraction increased the sensitivity of CE-LIF analysis; however, further optimization of methodology is necessary for accurate quantification of biological thiols in EBC.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

Surface-Engineered Nanomaterials for Optical Array Based Sensing

Array based sensing governed by optical methods provides fast and economic way for detection of wide variety of analytes where the ideality of detection processes depends on the sensor element's versatile mode of interaction with multiple analytes in an unbiased manner. This can be achieved by either the receptor unit having multiple recognition moiety, or their surface property should possess tuning ability upon fabrication called surface engineering. Nanomaterials have a high surface to volume ratio, making them viable candidates for molecule recognition through surface adsorption phenomena, which makes it ideal to meet the above requirements. Most crucially, by engineering a nanomaterial's surface, one may produce cross-reactive responses for a variety of analytes while focusing solely on a single nanomaterial. Depending on the nature of receptor elements, in the last decade the array-based sensing has been considering as multimodal detection platform which operates through various pathway including single channel, multichannel, binding and indicator displacement assay, sequential ON-OFF sensing, enzyme amplified and nanozyme based sensing etc. In this review we will deliver the working principle for Array-based sensing by using various nanomaterials like nanoparticles, nanosheets, nanodots and self-assembled nanomaterials and their surface functionality for suitable molecular recognition.

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.

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.

A dual-channel sensor array for discrimination of biothiols based on manganese dioxide nanosheets

A dual-signal sensor array for highly sensitive identification of biothiols is reported based on different optical responses of MnO₂/curcumin (CUR) system to different biothiols. The addition of MnO₂ nanosheets (MnO₂ NSs) quenches the fluorescence of CUR, and the color of the mixture changes from yellow to brown. In the presence of reductive biothiols, MnO₂ NSs are etched and lose their fluorescence quenching ability, resulting in an increase in the fluorescence intensity of CUR at 540 nm and a decrease in the absorbance at 430 nm. The sensor array generates specific response modes based on the varying reduction abilities of different biothiols, which can be distinguished by linear discriminant analysis (LDA). The sensor array successfully distinguished five biothiols (glutathione (GSH), dithiothreitol (DTT), cysteine (Cys), mercaptoethanol (ME), and homocysteine (Hcy)) across a wide concentration range (1 μM-100 μM) and biothiol mixtures with varing molar ratios.

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.

Optical sensor arrays for the detection and discrimination of natural products

Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.

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.

Use of metal nanoparticles for preconcentration and analysis of biological thiols

Metal nanoparticles (NPs) exhibit several unique physicochemical properties, including redox activity, surface plasmon resonance, ability to quench fluorescence, biocompatibility, or a high surface-to-volume ratio. They are being increasingly used in analysis and preconcentration of thiol containing compounds, because they are able to spontaneously form a stable Au/Ag/Cu-S dative bond. They thus find wide application in environmental and particularly in medical science, especially in the analysis of biological thiols, the endogenous compounds that play a significant role in many biological systems. In this review article, we provide an overview of various types of NPs that have been applied in analysis and preconcentration of biological thiols, mainly in human biological fluids. We first discuss shortly the types of NPs and their synthesis, properties, and their ability to interact with thiol compounds. Then we outline the sample preconcentration and analysis methods that were used for this purpose with special emphasis on optical, electrochemical, and separation techniques.

Ligand Exchange on MoS2 Nanosheets: Applications in Array-Based Sensing and Drug Delivery

Two-dimensional MoS2 nanosheets (2D-MoS2) have been widely used in many biological applications due to their distinctive physicochemical properties. Further, the development of surface modification using thiolated ligands allows us to use them for many specific applications. But the effect of possible ligand exchange on 2D-MoS2 has never been explored, which can play an important role in diverse biological applications. In this study, we have observed the ligand-exchange phenomenon on 2D-MoS2 in the presence of different thiolated ligands. The initial study proceeded with boron-dipyrromethene (BODIPY) functionalized MoS2 with different concentrations of glutathione (GSH), which is the most abundant thiol species in the cytoplasm of various cancer cells. It was found that in the presence of GSH the fluorescence of BODIPY can be regenerated, which is time and concentration dependent. We have also examined this phenomenon with different thiol ligands and transition-metal dichalcogenides (TMDs). We observed a variable rate of ligand exchange in different solvents, surface functionality, and receptor environments that helped us to construct sensor arrays. Interestingly, a ligand-exchange process was not observed in the presence of dithiols. Further, this concept was applied to a cancerous cell line for in vitro delivery. We found that BODIPY-functionalized 2D-MoS2 undergoes thiol exchange by intracellular GSH and subsequently enhanced the fluorescence in the cytoplasm of cancer cells. This strategy can be applied to the development of 2D-TMD-based materials for various biological applications related to ligand exchange.

Mixed-Ligand gold nanoparticles based optical sensor array for the recognition and quantification of seven toxic metals

Sensor arrays use pattern recognition for the identification and quantification of analytes. In the presented work, a gold nanoparticle (GNP) based optical sensor array was employed to classify and quantify seven toxic metals (arsenic, barium, cadmium, cerium, chromium, lead, and mercury). The sensor array receptors were GNPs functionalized by mercaptoundecanoic acid, 2-mercaptoethanesulfonate, and a 1:1 mixture of the two ligands. The mixed-ligand particle responds to the same analytes as the mono-ligand particles but in a distinctive way. This behavior demonstrates the high potential of mixed-ligand particles in the fabrication of sensor array receptors. The responses of the GNPs to different concentrations of the seven metal ions were analyzed, and a unique "classification trajectory" was produced for every metal. Samples of different metal concentrations were then measured and identified using the "classification trajectories". Once sample composition has been identified, a PLSR model, produced from the concatenated sensor array spectra of four calibration samples for each nanoparticle, was used to determine the metal concentration.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

Two-Dimensional Material-Based Colorimetric Biosensors: A Review

Two-dimensional (2D) materials such as graphene, graphene oxide, transition metal oxide, MXene and others have shown high potential for the design and fabrication of various sensors and biosensors due to their 2D layered structure and unique properties. Compared to traditional fluorescent, electrochemical, and electrical biosensors, colorimetric biosensors exhibit several advantages including naked-eye determination, low cost, quick response, and easy fabrication. In this review, we present recent advances in the design, fabrication, and applications of 2D material-based high-performance colorimetric biosensors. Potential colorimetric sensing mechanisms and optimal material selection as well as sensor fabrication are introduced in brief. In addition, colorimetric biosensors based on different 2D materials such as graphene, transition metal dichalcogenide/oxide, MXenes, metal-organic frameworks, and metal nanoplates for the sensitive detection of DNA, proteins, viruses, small molecules, metallic ions, and others are presented and discussed in detail. This work will be helpful for readers to understand the knowledge of 2D material modification, nanozymes, and the synthesis of hybrid materials; meanwhile, it could be valuable to promote the design, fabrication, and applications of 2D material-based sensors and biosensors in quick bioanalysis and disease diagnostics.

Toward Food Freshness Monitoring: Coordination Binding-Based Colorimetric Sensor Array for Sulfur-Containing Amino Acids

Herein, a self-assembled colorimetric chemosensor array composed of off-the-shelf catechol dyes and a metal ion (i.e., Zn2+) has been used for the sulfur-containing amino acids (SCAAs; i.e., glutathione, glutathione disulfide, L-cysteine, DL-homocysteine, and L-cystine). The coordination binding-based chemosensor array (CBSA) fabricated by a competitive assay among SCAAs, Zn2+ ions, and catechol dyes [i.e., pyrocatechol violet (PV), bromopyrogallol red (BPR), pyrogallol red (PR), and alizarin red S (ARS)] yielded fingerprint-like colorimetric changes. We succeeded in the qualification of SCAAs based on pattern recognition [i.e., a linear discrimination analysis (LDA)] with 100% correct classification accuracy. The semiquantification of reduced/oxidized forms of SCAAs was also performed based on LDA. Furthermore, we carried out a spike test of glutathione in food samples using the proposed chemosensor array with regression analysis. It is worth mentioning that we achieved a 91-110% recovery rate in real sample tests, which confirmed the accuracy of the constructed model. Thus, this study represents a step forward in assessing food freshness based on supramolecular analytical methods.

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.

Fabrication of a sensitive colorimetric nanosensor for determination of cysteine in human serum and urine samples based on magnetic-sulfur, nitrogen graphene quantum dots as a selective platform and Au nanoparticles

A novel colorimetric nanosensor is reported for the selective and sensitive determination of cysteine using magnetic-sulfur, nitrogen graphene quantum dots (Fe₃O₄/S, N-GQDs), and gold nanoparticles (Au NPs). Thus, S, N-GQDs was firstly immobilized on Fe₃O₄ nanoparticles through its magnetization in the presence of Fe³⁺ in the alkali solution. The prepared Fe₃O₄/S, N-GQDs were dispersed in cysteine solution resulting in its quick adsorption on the surface of the Fe₃O₄/S, N-GQDs through hydrogen bonding interaction. Then, Au NPs solution was added to this mixture that after a short time, the color of Au NPs changed from red to blue, the intensity of surface plasmon resonance peak of Au NPs at 530 nm decreased, and a new peak at a higher wavelength of 680 nm appeared. The effective parameters on cysteine quantification were optimized via response surface methodology using the central composite design. Under optimum conditions, the absorbance ratio (A₆₈₀/A₅₃₀) has a linear proportionality with cysteine concentration in the range of 0.04-1.20 μmol L^-1 with a limit of detection of 0.009 μmol L^-1. The fabrication of the reported nanosensor is simple, fast, and is capable of efficient quantification of ultra traces of cysteine in human serum and urine real samples.

Development of a colorimetric sensor array based on monometallic and bimetallic nanoparticles for discrimination of triazole fungicides

Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns that can be used for the detection and discrimination of triazoles. Pattern recognition methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis, have been employed for the differentiation of triazoles in the concentration range of 0.1-0.55 μg mL^-1. Besides, the sensor array demonstrates promising practicability to satisfactorily distinguished triazole in mixtures and complex media of wheat flour and cucumber samples. The proposed colorimetric sensor array might pave the way towards a cost-effective and rapid, yet sensitive platform for high-throughput monitoring of residual amounts of pesticides for on-site applications.

A single-nanozyme colorimetric array based on target-induced differential surface passivation for quantification and discrimination of Cl-, Br- and I- ions

Abnormal levels of halide ions in drinking water have enormous threats to human health, and thus designing reliable and sensitive methods to quantify and distinguish these ions becomes extremely crucial. Herein, we develop a single-nanozyme colorimetric array based on target-induced differential surface passivation for the quantification and discrimination of Cl^-, Br^- and I^- ions. Silver citrate (Ag₃Cit) is designed as an oxidase mimic to efficiently catalyze the 3,3',5,5'-tetramethylbenzidine (TMB) chromogenic reaction. When halide ions (Cl^-, Br^- and I^-) are present, due to their different precipitation interactions with the Ag(Ⅰ) entity in Ag₃Cit, they can passivate the active surface of the nanozyme to various degrees, resulting in the inhibited TMB chromogenic reaction differentially. According to this principle, simple and efficient quantitative detection of Cl^-, Br^- and I^- ions was achieved, with all the detection limits down to the nM level. By employing Ag₃Cit as a single sensing element, a nanozyme catalysis-based colorimetric array was further established, and both individual and mixed ions were successfully distinguished by integrating the array with principal component analysis. Accurate identification of unknown samples was also verified via a double-blind protocol, indicating potential applications of the array in practice.

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.

Visual detection of multiple antioxidants based on three chloroauric acid/Au-Ag nanocubes

A colorimetric sensing method is described for discrimination of multiple antioxidants based on core-shell Au@Ag nanocubes (NCs). In order to extract data-rich colorimetric responses from the sensor array, three different concentrations of chloroaurate acid (HAuCl₄) were employed as sensing elements. Interestingly, Au³⁺ ions can be reduced to different valence states (i.e., Au(0) and Au(I)) by different antioxidants, and thus effectively inhibit the oxidation etching process of Au@Ag NCs by Au(III) ions to varying extents, generating diverse colorimetric responses (color and absorbance). This enables identification of the six antioxidants at 10 nM via linear discriminant analysis (LDA) with relative standard deviation (RSD) of 2.52% (n = 3). The discrimination ability of the sensor array was further evaluated in antioxidant binary and multicomponent mixtures. Remarkably, identification of these six antioxidants spiked in urine was realized with 100% of accuracy. Schematic presentation of colorimetric assay for antioxidants based on three chloroauric acid/Au-Ag nanocubes.

Chemical sensing with Au and Ag nanoparticles

Noble metal nanoparticles (NPs) are ideal scaffolds for the fabrication of sensing devices because of their high surface-to-volume ratio combined with their unique optical and electrical properties which are extremely sensitive to changes in the environment. Such characteristics guarantee high sensitivity in sensing processes. Metal NPs can be decorated with ad hoc molecular building blocks which can act as receptors of specific analytes. By pursuing this strategy, and by taking full advantage of the specificity of supramolecular recognition events, highly selective sensing devices can be fabricated. Besides, noble metal NPs can also be a pivotal element for the fabrication of chemical nose/tongue sensors to target complex mixtures of analytes. This review highlights the most enlightening strategies developed during the last decade, towards the fabrication of chemical sensors with either optical or electrical readout combining high sensitivity and selectivity, along with fast response and full reversibility, with special attention to approaches that enable efficient environmental and health monitoring.

Wearable devices and IoT applications for symptom detection, infection tracking, and diffusion containment of the COVID-19 pandemic: a survey

Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics.

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.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Design of a pseudo stir bar sorptive extraction using graphenized pencil lead as the base of the molecularly imprinted polymer for extraction of nabumetone

Molecularly imprinted polymer (MIP) was synthesized through the coprecipitation method on the graphene oxide anchored pencil lead as a substrate for the first time and applied as an efficient sorbent for pseudo stir bar sorptive extraction of nabumetone. The extracted analyte was determined by a novel spectrophotometric method based on the aggregation of silicate sol-gel stabilized silver nanoparticles in the presence of the analyte. The synthesized polymer was characterized using Fourier transform infrared spectroscopy and field emission scanning electron microscopy. Optimization of important parameters affecting the extraction efficiency was done using central composite design whereas the spectrophotometric method was optimized via one at a time variable. Under the optimal conditions, the calibration curve exhibited linearity in the concentration range of 1.5-20.0 μg L^-1. A limit of detection of 0.20 μg L^-1, an enhancement factor of 393 and relative standard deviations (at 10 μg L^-1, n = 6) of 4.6% and 8.1% for intra- and inter-day analysis were obtained. The developed procedure was successfully utilized for the quantification of traces of nabumetone in tap water and biological samples with the complex matrix including human urine and serum.

Point-of-Use Rapid Detection of SARS-CoV-2: Nanotechnology-Enabled Solutions for the COVID-19 Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic that has been spreading around the world since December 2019. More than 10 million affected cases and more than half a million deaths have been reported so far, while no vaccine is yet available as a treatment. Considering the global healthcare urgency, several techniques, including whole genome sequencing and computed tomography imaging have been employed for diagnosing infected people. Considerable efforts are also directed at detecting and preventing different modes of community transmission. Among them is the rapid detection of virus presence on different surfaces with which people may come in contact. Detection based on non-contact optical techniques is very helpful in managing the spread of the virus, and to aid in the disinfection of surfaces. Nanomaterial-based methods are proven suitable for rapid detection. Given the immense need for science led innovative solutions, this manuscript critically reviews recent literature to specifically illustrate nano-engineered effective and rapid solutions. In addition, all the different techniques are critically analyzed, compared, and contrasted to identify the most promising methods. Moreover, promising research ideas for high accuracy of detection in trace concentrations, via color change and light-sensitive nanostructures, to assist fingerprint techniques (to identify the virus at the contact surface of the gas and solid phase) are also presented.

Quantum dot (QD)-based probes for multiplexed determination of heavy metal ions

Heavy metal contamination is a major global concern and additive toxicity resulting from the exposure to multiple heavy metal ions is more pronounced than that induced by a single metal species. Quantum dots (QDs) have demonstrated unique properties as sensing materials for heavy metal ions over the past two decades. With the rapid development and deep understanding on determination of single heavy metal ion using QD probes, this technology has been employed for sensing multiple metal ions. This review (with 97 refs.) summarizes the progress made in recent years in methods for multiplexed determination of heavy metal ions using QDs. Following an introduction into the importance of simultaneous quantitation of multiple heavy metal ions in environmentally relevant settings, the review discusses the applications of different types of QDs, i.e. chalcogenide, carbon, polymer and graphene in this field. Determination strategies based on fluorometric, colorimetric and electrochemical responses were reviewed including the testing mechanisms and differentiation between various metal ions. In addition, current state of the art sensor constructions, i.e. immobilization of QDs on solid substrate and sensor arrays have been highlighted. A concluding section describes the limitations, opportunities and future challenges of the QD probes. We also compiled a comprehensive table of currently available literature. The listed papers provided information in the following categories, i.e. type of QDs used, ligands or other components in the probe, metal ions tested, medium/substrate of the probe, transduction methods, discrimination mechanism, limit of detection (LOD) and concentration range. Graphic abstract.

Colorimetric Differentiation of Multiple Oxidizing Anions Based on Two Core-Shell Au@Ag Nanoparticles with Different Morphologies as Array Recognition Elements

The efficient discrimination of oxidizing anions is of considerable importance in environmental monitoring. Here, for the first time, we have developed a simple and fast colorimetric sensor array for detection and identification of oxidizing anions, which takes advantage of the etching of the Ag shell of two core-shell Au@Ag nanoparticles (Au@Ag nanospheres (Au@Ag NPs) and Au@Ag nanocubes (Au@Ag NCs)) by oxidizing anions. The differential etching ability of various oxidizing anions to the Ag shell of the two Au@Ag nanoparticles resulted in different absorbance and color change of the nanoparticles. Thus, employing Au@Ag NPs and Au@Ag NCs as the array's receptors and the indicators, six oxidizing anions (i.e., BrO₃^-, Cr₂O₇^2-, ClO₄^-, IO₃^-, IO₄^-, and MnO₄^-) down to 10 nM could be identified from each other by their own colorimetric response patterns. Moreover, the complex mixtures of oxidizing anions could be well discriminated. Most importantly, the sensor array was successfully applied to the discrimination of oxidizing anions in river water and tap water samples.

Electrostatically controlled plasmonic effects of gold nanoparticles with indigo-carmine functionation for rapid and straightforward colorimetric detection of Cu2+ ions

A colorimetric sensor is fabricated for effective on-site monitoring of Cu²⁺ ions content based on the distance-dependent optical properties of gold nanoparticles-polyvinyl alcohol-citrate (Au-NPs-PVA-Cy) which plasmonic effect electrostatically was controlled by PVA-Cy stabilizing indigo-carmine (IC) functionalizing. The surface-modified gold nanoparticles were extremely stable with a strong affinity toward Cu²⁺ ions. Citrate ion was employed as a cross-linking agent for pairs of Au-NPs-PVA-Cy and IC for stabilizing coordination between Cu²⁺ ion and IC. The active materials were characterized by UV-Vis, SEM, DLS, XRD, FT-IR, and EDS analyses. The sensor response toward Cu²⁺ ion was found to be linear in the range of 0.0974 to 3.27 μM with the limit of detection and quantification values of 0.021 and 0.07 μM, respectively. The sensor represents good sensitivity and stability, promisingly suggesting this device for the accurate and repeatable determination of Cu²⁺ in real water samples. The effect of different foreign ions on the selectivity of the sensor was checked. The sensor has a long shelf life in comparison to other similar colorimetric sensors. Also, it shows a repeatable response with RSD% of 2.02%. Thus, the sensing of Cu²⁺ ions based on the electrostatically control plasmonic of Au-NPs-PVA-Cy was developed with proper signaling based on the color change from dark blue to light blue as readily seen by the naked eye. Furthermore, the efficient environmental applicability of this simple and rapid determination of the Cu²⁺ sensor is proved.

Ultrasensitive Physical, Bio, and Chemical Sensors Derived from 1-, 2-, and 3-D Nanocellulosic Materials

Sensors are of increasing interest since they can be applied to daily life in different areas from various industrial sectors. As a natural nanomaterial, nanocellulose plays a vital role in the development of novel sensors, particularly in the context of constructing multidimensional architectures. This review summarizes the utilization of nanocellulose including cellulose nanofibers, cellulose nanocrystals, and bacterial cellulose for sensor design, mainly focusing on the influence of nanocellulose on the sensing performance of these sensors. Special attention is paid to nanocellulose in different forms (1D, 2D, and 3D) to highlight the impact of nanocellulose constructed structures. The aim is to provide a critical review on the most recent progress (especially after 2017) related to nanocellulose-containing sensors, since there are significantly increasing research activities in this area. Moreover, the outlook for the development of nanocellulose-containing sensors is also provided at the end of this work.

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.

Smart phone assisted, rapid, simplistic, straightforward and sensitive biosensing of cysteine over other essential amino acids by β-cyclodextrin functionalized gold nanoparticles as a colorimetric probe

Herein, we have attempted the synthesis of β-CD functionalized AuNPs and then applied them as a colorimetric assay for the quantification of Cys over other different essential amino acids.

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.

Irreversible photo-Fenton-like triggered agglomeration of ultra-small gold nanoparticles capped with crosslinkable materials

A photo-Fenton-like process can promote the agglomeration and LSPR red-shifting of ultra-small gold nanoparticles by triggering a crosslink-degradation pathway that involves the surface coating, Fe(iii)-citrate and hydrogen peroxide. Applications may range from controlled photo-deposition of active materials to asynchronous sensing technologies to light-focused microfabrication.