Colorimetric Sensor Array Based on Gold Nanoparticles with Diverse Surface Charges for Microorganisms Identification

Array-based specific classification of bacterial species via ligands with dimethylamino/amino groups

The early detection of bacterial species plays a crucial role in patient prognosis and the development of effective therapeutic regimens. This study introduces an accessible and promising colorimetric sensor array designed to classify gram-positive (G+) and gram-negative (G-) bacterial species. The classification relies on 6 chemical ligands with dimethylamino/amino groups as sensing elements and silver nanotriangles as colorimetric probes. Using these specific sensor arrays, we successfully differentiated G- and G+ bacterial species and discriminated individual bacterial strains, and the sensors exhibited remarkable reproducibility and high sensitivity. Moreover, the sensor array can identify bacterial mixtures and bacteria at varying concentrations, underscoring its versatility. In summary, this sensor array offers an effective tool for bacterial analysis with promising applications in the field of biomedical diagnostics.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

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.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

A review on the chemical and biological sensing applications of silver/carbon dots nanocomposites with their interaction mechanisms

The development of new nanocomposites has a significant impact on modern instrumentation and analytical methods for chemical analysis. Due to their unique properties, carbon dots (CDs) and silver nanoparticles (AgNPs), distinguished by their unique physical, electrochemical, and optical properties, have captivated significant attention. Thus, combining AgNPs and CDs may produce Ag/CDs nanocomposites with improved performances than the individual material. This comprehensive review offers an in-depth exploration of the synthesis, formation mechanism, properties, and the recent surge in chemical and biological sensing applications of Ag/CDs with their sensing mechanisms. Detailed insights into synthesis methods to produce Ag/CDs are unveiled, followed by information on their physicochemical and optical properties. The crux of this review lies in its spotlight on the diverse landscape of chemical and biological sensing applications of Ag/CDs, with a particular focus on fluorescence, electrochemical, colorimetric, surface-enhanced Raman spectroscopy, and surface plasmon resonance sensing techniques. The elucidation of sensing mechanisms of the nanocomposites with various target analytes adds depth to the discussion. Finally, this review culminates with a concise summary and a glimpse into future perspectives of Ag/CDs aiming to achieve highly efficient and enduring Ag/CDs for various applications.

Colorimetric detection of oral bacteria using functionalized gold nanoparticles as a plasmonic biosensor array

Early detection of specific oral bacterial species would enable timely treatment and prevention of certain oral diseases. In this work, we investigated the sensitivity and specificity of functionalized gold nanoparticles for plasmonic sensing of oral bacteria. This approach is based on the aggregation of positively charged gold nanoparticles on the negatively charged bacteria surface and the corresponding localized surface plasmon resonance (LSPR) shift. Gold nanoparticles were synthesized in different sizes, shapes and functionalization. A biosensor array was developed consisting of spherical- and anisotropic-shaped (1-hexadecyl) trimethylammonium bromide (CTAB) and spherical mercaptoethylamine (MEA) gold nanoparticles. It was used to detect four oral bacterial species (Aggregatibacter actinomycetemcomitans, Actinomyces naeslundii, Porphyromonas gingivalis and Streptococcus oralis). The plasmonic response was measured and analysed using RGB and UV-vis absorbance values. Both methods successfully detected the individual bacterial species based on their unique responses to the biosensor array. We present an in-depth study relating the bacteria zeta potential and AuNP aggregation to plasmonic response. The sensitivity depends on multiple parameters, such as bacterial species and concentration as well as gold nanoparticle shape, concentration and functionalization.

Nanomedicine for the Detection and Treatment of Ocular Bacterial Infections

Ocular bacterial infection is a prevalent cause of blindness worldwide, with substantial consequences for normal human life. Traditional treatments for ocular bacterial infections areless effective, necessitating the development of novel techniques to enable accurate diagnosis, precise drug delivery, and effective treatment alternatives. With the rapid advancement of nanoscience and biomedicine, increasing emphasis has been placed on multifunctional nanosystems to overcome the challenges posed by ocular bacterial infections. Given the advantages of nanotechnology in the biomedical industry, it can be utilized to diagnose ocular bacterial infections, administer medications, and treat them. In this review, the recent advancements in nanosystems for the detection and treatment of ocular bacterial infections are discussed; this includes the latest application scenarios of nanomaterials for ocular bacterial infections, in addition to the impact of their essential characteristics on bioavailability, tissue permeability, and inflammatory microenvironment. Through an in-depth investigation into the effect of sophisticated ocular barriers, antibacterial drug formulations, and ocular metabolism on drug delivery systems, this review highlights the challenges faced by ophthalmic medicine and encourages basic research and future clinical transformation based on ophthalmic antibacterial nanomedicine.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Preparation and usage of nanomaterials in biomedicine

Nanotechnology is one of the most promising and decisive technologies in the world. Nanomaterials, as the primary research aspect of nanotechnology, are quite different from macroscopic materials because of their unique optical, electrical, magnetic, thermal properties, and more robust mechanical properties, which make them play an essential role in the field of materials science, biomedical field, aerospace field, and environmental energy. Different preparation methods for nanomaterials have various physical and chemical properties and are widely used in different areas. In this review, we focused on the preparation methods, including chemical, physical, and biological methods due to the properties of nanomaterials. We mainly clarified the characteristics, advantages, and disadvantages of different preparation methods. Then, we focused on the applications of nanomaterials in biomedicine, including biological detection, tumor diagnosis, and disease treatment, which provide a development trend and promising prospects for nanomaterials.

Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.

Detection and discrimination of pathogenic bacteria with nanomaterials-based optical biosensors: A review

Pathogenic bacteria can pose a great threat to food safety and human health. It is therefore imperative to develop a rapid, portable, and sensitive determination and discrimination method for pathogenic bacteria. Over the past few years, various nanomaterials (NMs) have been employed as desirable nanoprobes because they possess extraordinary properties that can be used for optical signal enabled detection and identification of bacteria. By means of modification, NMs can, depending on different mechanisms, sense targets directly or indirectly, which then provides an essential support for the detection and differentiation of pathogenic bacteria. In this review, recent application of NMs-based optical biosensors for food safety bacterial detection and discrimination is performed, mainly in but not limited to noble metal NMs, fluorescent NMs, and point-of-care testing (POCT). This review also focuses on future trends in bacterial detection and discrimination, and machine learning in performing intelligent rapid detection and multiple accurate identification of bacteria.

Fluorimetric identification of sulfonamides by carbon dots embedded photonic crystal molecularly imprinted sensor array

Identification of sulfonamides (SAs) residues in food is vital for human health. A set of 4-channel sensor array was constructed by carbon dots (CDs) embedded in photonic crystal molecularly imprinted (PCMIP@CDs) film which included 3 PCMIP@CDs units and 1 PCNIP@CDs unit to determine typical SAs: sulfadimethoxine, sulfathiazole, sulfaguanidine, sulfamethazine, sulfadiazine. Under the optimal conditions, the response time of the sensor array was only 200 s. Moreover, 300 fluorescence response signals (4 sensor units × 5 sulfonamides × 3 concentrations × 5 repeats) were processed by pattern recognition technique to analyze the ability of the sensor array to recognize 5 kinds of SAs. Subsequently, the linear discrimination analysis (LDA) method was used to identify the five SAs simultaneously with 100 % classification accuracy and the limit of detection was 0.01-0.26 nmol/L. Moreover, the proposed method can effectively identify-five SAs in water and fish samples.

Identification of Proteins Using Supramolecular Gold Nanoparticle-Dye Sensor Arrays

The rapid detection of proteins is very important in the early diagnosis of diseases. Gold nanoparticles (AuNPs) can be engineered to bind biomolecules efficiently and differentially. Cross-reactive sensor arrays have high sensitivity for sensing proteins using differential interactions between sensor elements and bioanalytes. A new sensor array was fabricated using surface-charged AuNPs with dyes supramolecularly encapsulated into the AuNP monolayer. The fluorescence of dyes is partially quenched by the AuNPs and can be restored or further quenched due to the differential interactions between AuNPs with proteins. This sensing system enables the discrimination of proteins in both buffer and human serum, providing a potential tool for real-world disease diagnostics.

Red-to-blue colorimetric probe based on biomass carbon dots for smartphone-integrated optosensing of Cu(II) and L-cysteine

We constructed a smartphone-integrated optosensor with inexpensive, reversible, environmental friendly, and rapid adsorption to detect Cu(II) and L-cysteine (L-Cys). The key part of this study was to prepare a red-to-blue colorimetric probe from herbaceous andrographis paniculata using one-pot polymerization at room temperature. When Cu(II) existed, the red fluorescence on the surface of the core-shell probe was quenched, while the blue fluorescence of the core did not respond, because the colorimetric probe interacted with the Cu(II) on the surface of red CDs. After L-Cys added, it interacted with the Cu(II) to strip it from the surface of red CDs, resulting in the recovery of fluorescence response. Under optimal conditions, the detection limits of this method for Cu(II) and L-Cys were 71 nM and 12 nM, respectively. Further, the red-to-blue colorimetric probe was integrated into smartphone with a software application to convert fluorescent color images into specific red (R), green (G), and blue (B) values. The spiked recovery of Cu(II) and L-Cys in lake water was verified the feasibility of the developed optosensors with a recovery of 98.2-101.6 % and 103.3-121.6 %. This method for detecting Cu(II) and L-Cys can not only recognize metal ions from actual samples, but also effectively protect CDs from quenching and restore fluorescence.

Recent Progress in Sensor Arrays: From Construction Principles of Sensing Elements to Applications

The traditional sensors are designed based on the "lock-and-key" strategy with high selectivity and specificity for detecting specific analytes, which however are not suitable for detecting multiple analytes simultaneously. With the help of pattern recognition technologies, the sensor arrays excel in distinguishing subtle changes caused by multitarget analytes with similar structures in a complex system. To construct a sensor array, the multiple sensing elements are undoubtedly indispensable units that will selectively interact with targets to generate the unique "fingerprints" based on the distinct responses, enabling the identification among various analytes through pattern recognition methods. This comprehensive review mainly focuses on the construction strategies and principles of sensing elements, as well as the applications of sensor array for identification and detection of target analytes in a wide range of fields. Furthermore, the present challenges and further perspectives of sensor arrays are discussed in detail.

Identification of Antibiotic Resistance in ESKAPE Pathogens through Plasmonic Nanosensors and Machine Learning

Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention and control of nosocomial infections. However, current techniques like genotype identification and antibiotic susceptibility testing are generally time-consuming and require large-scale equipment. Herein, we develop a rapid, facile, and sensitive technique to determine the antibiotic resistance phenotype among ESKAPE pathogens through plasmonic nanosensors and machine learning. Key to this technique is the plasmonic sensor array that contains gold nanoparticles functionalized with peptides differing in hydrophobicity and surface charge. The plasmonic nanosensors can interact with pathogens to generate bacterial fingerprints that alter the surface plasmon resonance (SPR) spectra of nanoparticles. In combination with machine learning, it enables the identification of antibiotic resistance among 12 ESKAPE pathogens in less than 20 min with an overall accuracy of 89.74%. This machine-learning-based approach allows for the identification of antibiotic-resistant pathogens from patients and holds great promise as a clinical tool for biomedical diagnosis.

A single thiolated-phage displayed nanobody-based biosensor for label-free detection of foodborne pathogen

Rapid and sensitive detection of bacterial pathogens present in food and environmental samples is of crucial importance to ensure human health and safety. Here, we present a one-step label-free colorimetric strategy based on M13 bacteriophage-displayed nanobody (phage-Nb) derived from camelid heavy-chain antibodies specific to Vibrio parahaemolyticus (V. parahaemolyticus). The thiolation of phage-Nb (Phage-Nb-SH) on pVIII shell proteins induces the aggregation of gold nanoparticles (AuNPs), whereas the specific interaction between nanobody and bacteria prevents the aggregation of AuNPs, resulting in visible color change due to alteration of surface plasmon resonance properties. Based on this phenomenon, a simple and sensitive colorimetric immunosensor for V. parahaemolyticus was developed. The assay can be accomplished within 100 min, and exhibits a visual detection limit of 10⁴ cfu/mL and a quantitative detection limit of 10³ cfu/mL, with no cross-reactivity towards other bacterial species. This strategy takes full advantages of both the high specificity of phage-Nbs and the optical properties of AuNPs, enabling simple and rapid detection of bacterial pathogens.

Optical reflectometric measurement of SARS-CoV-2 (COVID-19) RNA based on cationic cysteamine-capped gold nanoparticles

The coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a major public health outbreak in late 2019 and was proclaimed a global pandemic in March 2020. A reflectometric-based RNA biosensor was developed by using cysteamine-stabilized gold nanoparticles (cysAuNPs) as the colorimetric probe for bioassay of COVID-19 RNA (SARS-CoV-2 RNA) sequence. The cysAuNPs aggregated in the presence of DNA probes via cationic and anionic electrostatic attraction between the positively charged cysteamine ligands and the negatively charged sugar-phosphate backbone of DNA, whilst in the presence of target RNAs, the specific recognition between DNA probes and targets depleted the electrostatic interaction between the DNA probes and cysAuNPs signal probe, leading to dispersed particles. This has rendered a remarkable shifting in the surface plasmon resonance (SPR) on the basis of visual color change of the RNA biosensor from red to purplish hue at the wavelength of 765 nm. Optical evaluation of SARS-CoV-2 RNA by means on reflectance transduction of the RNA biosensor based on cysAuNPs optical sensing probes demonstrated rapid response time of 30 min with high sensitivity, good linearity and high reproducibility across a COVID-19 RNA concentration range of 25 nM to 200 nM, and limit of detection (LOD) at 0.12 nM. qPCR amplification of SARS-CoV-2 viral RNA showed good agreement with the proposed RNA biosensor by using spiked RNA samples of the oropharyngeal swab from COVID-19 patients. Therefore, this assay is useful for rapid and early diagnosis of COVID-19 disease including asymptomatic carriers with low viral load even in the presence of co-infection with other viruses that manifest similar respiratory symptoms.

Recent Advances in Colorimetric Sensors Based on Gold Nanoparticles for Pathogen Detection

Infectious pathogens cause severe threats to public health due to their frightening infectivity and lethal capacity. Rapid and accurate detection of pathogens is of great significance for preventing their infection. Gold nanoparticles have drawn considerable attention in colorimetric biosensing during the past decades due to their unique physicochemical properties. Colorimetric diagnosis platforms based on functionalized AuNPs are emerging as a promising pathogen-analysis technique with the merits of high sensitivity, low-cost, and easy operation. This review summarizes the recent development in this field. We first introduce the significance of detecting pathogens and the characteristics of gold nanoparticles. Four types of colorimetric strategies, including the application of indirect target-mediated aggregation, chromogenic substrate-mediated catalytic activity, point-of-care testing (POCT) devices, and machine learning-assisted colorimetric sensor arrays, are systematically introduced. In particular, three biomolecule-functionalized AuNP-based colorimetric sensors are described in detail. Finally, we conclude by presenting our subjective views on the present challenges and some appropriate suggestions for future research directions of colorimetric sensors.

Recent Advances in Nanotechnology for the Management of Klebsiella pneumoniae-Related Infections

Klebsiella pneumoniae is an important human pathogen that causes diseases such as urinary tract infections, pneumonia, bloodstream infections, bacteremia, and sepsis. The rise of multidrug-resistant strains has severely limited the available treatments for K. pneumoniae infections. On the other hand, K. pneumoniae activity (and related infections) urgently requires improved management strategies. A growing number of medical applications are using nanotechnology, which uses materials with atomic or molecular dimensions, to diagnose, eliminate, or reduce the activity of different infections. In this review, we start with the traditional treatment and detection method for K. pneumoniae and then concentrate on selected studies (2015-2022) that investigated the application of nanoparticles separately and in combination with other techniques against K. pneumoniae.

Colorimetric sensor arrays for the differentiation of baijiu based on amino-acid-modified gold nanoparticles

It is of great significance for quality control to realize the discrimination for baijiu from different brands and origins. Strong-aroma-type baijiu (SAB), one of the most important Chinese aroma-type baijiu, exhibits the largest variety and market share. In this study, we proposed colorimetric sensor arrays based on gold nanoparticles (AuNPs) modified with different amino acids (AAs) to recognize the organic acids, and further distinguish different SABs. Three representative AAs, namely methionine (Met), tryptophan (Trp), and histidine (His), were selected to modify the AuNPs surface. The investigation of the effect of the main ingredients of SAB on AA@AuNPs aggregation confirmed that this aggregation mainly resulted from organic acids. Moreover, this aggregation was successfully used for differentiating 11 organic acids. Different pH conditions can not only cause changes of the content of organic acids in baijiu, but also disrupt the balance among flavor substances of baijiu to some extent. Consequently, the AA@AuNPs arrays under two pH conditions have been successfully applied to distinguish 14 kinds of SABs from different brands and origins. The proposed colorimetric sensor method is simple, rapid, and visualized and provides a potential application prospect for the quality control of baijiu and other alcoholic beverages.

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10⁵ ~ 10⁸ CFU/mL for Escherichia coli, 10² ~ 10⁷ CFU/mL for E. coli-β, 10³ ~ 10⁸ CFU/mL for Staphylococcus aureus, 10³ ~ 10⁷ CFU/mL for MRSA, 10² ~ 10⁸ CFU/mL for Pseudomonas aeruginosa, 10³ ~ 10⁸ CFU/mL for Enterococcus faecalis, 10² ~ 10⁸ CFU/mL for Klebsiella pneumoniae, and 10³ ~ 10⁸ CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.

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.

Sensor Heavy Metal from Natural Resources for a Green Environment: A Review Relation Between Synthesis Method and Luminescence Properties of Carbon Dots

Carbon dots are 10-nm nanomaterial classes as excellent candidates in various applications: physics, biology, chemistry, and food science due to high stable biocompatibility and high surface expansive. Carbon dots (CDs) produced from natural materials have received wide attention due to their unique benefits, easy availabilities, sufficient costs, and harmless to the ecosystem. The various properties of CDs can be obtained from various synthesis methods: hydrothermal, microwave-assisted, and pyrolysis. The CDs have shown enormous potential in metal particle detection, colorimetric sensors, electrochemical sensors, and pesticide sensor. This review provides systematic information on a synthesis method based on natural resources and the application to the environmental sensors for supporting the clean environment. We hopefully this review, useful as a reference source in providing the guidance or roadmap of new researchers to develop new strategy in increasing luminescence properties CDs for multi detection of heavy metal in the environment.

Fluorescent-based nanosensors for selective detection of a wide range of biological macromolecules: A comprehensive review

Thanks to their unique attributes, such as good sensitivity, selectivity, high surface-to-volume ratio, and versatile optical and electronic properties, fluorescent-based bioprobes have been used to create highly sensitive nanobiosensors to detect various biological and chemical agents. These sensors are superior to other analytical instrumentation techniques like gas chromatography, high-performance liquid chromatography, and capillary electrophoresis for being biodegradable, eco-friendly, and more economical, operational, and cost-effective. Moreover, several reports have also highlighted their application in the early detection of biomarkers associated with drug-induced organ damage such as liver, kidney, or lungs. In the present work, we comprehensively overviewed the electrochemical sensors that employ nanomaterials (nanoparticles/colloids or quantum dots, carbon dots, or nanoscaled metal-organic frameworks, etc.) to detect a variety of biological macromolecules based on fluorescent emission spectra. In addition, the most important mechanisms and methods to sense amino acids, protein, peptides, enzymes, carbohydrates, neurotransmitters, nucleic acids, vitamins, ions, metals, and electrolytes, blood gases, drugs (i.e., anti-inflammatory agents and antibiotics), toxins, alkaloids, antioxidants, cancer biomarkers, urinary metabolites (i.e., urea, uric acid, and creatinine), and pathogenic microorganisms were outlined and compared in terms of their selectivity and sensitivity. Altogether, the small dimensions and capability of these nanosensors for sensitive, label-free, real-time sensing of chemical, biological, and pharmaceutical agents could be used in array-based screening and in-vitro or in-vivo diagnostics. Although fluorescent nanoprobes are widely applied in determining biological macromolecules, unfortunately, they present many challenges and limitations. Efforts must be made to minimize such limitations in utilizing such nanobiosensors with an emphasis on their commercial developments. We believe that the current review can foster the wider incorporation of nanomedicine and will be of particular interest to researchers working on fluorescence technology, material chemistry, coordination polymers, and related research areas.

Multiplexed Identification of Bacterial Biofilm Infections Based on Machine-Learning-Aided Lanthanide Encoding

Pathogenic biofilms are up to 1000-fold more drug-resistant than planktonic pathogens and cause about 80% of all chronic infections worldwide. The lack of prompt and reliable biofilm identification methods seriously prohibits the diagnosis and treatment of biofilm infections. Here, we developed a machine-learning-aided cocktail assay for prompt and reliable biofilm detection. Lanthanide nanoparticles with different emissions, surface charges, and hydrophilicity are formulated into the cocktail kits. The lanthanide nanoparticles in the cocktail kits can offer competitive interactions with the biofilm and further maximize the charge and hydrophilicity differences between biofilms. The physicochemical heterogeneities of biofilms were transformed into luminescence intensity at different wavelengths by the cocktail kits. The luminescence signals were used as learning data to train the random forest algorithm, and the algorithm could identify the unknown biofilms within minutes after training. Electrostatic attractions and hydrophobic-hydrophobic interactions were demonstrated to dominate the binding of the cocktail kits to the biofilms. By rationally designing the charge and hydrophilicity of the cocktail kit, unknown biofilms of pathogenic clinical isolates were identified with an overall accuracy of over 80% based on the random forest algorithm. Moreover, the antibiotic-loaded cocktail nanoprobes efficiently eradicated biofilms since the nanoprobes could penetrate deep into the biofilms. This work can serve as a reliable technique for the diagnosis of biofilm infections and it can also provide instructions for the design of multiplex assays for detecting biochemical compounds beyond biofilms.

New insights for integration of nano particle with microfluidic systems for sensor applications

A biosensor is a compact device, which utilizes biological derived recognition component, immobilized on a transducer to analyze an analyte. Nanoparticles with their unique chemical and physical properties are versatile in their applications to develop as sensors. Different nanoparticles play different roles in the sensing systems like metal and metal oxide nanoparticles. The application of Gold, Silver and Copper nanoparticles will be discussed in brief. The nanoparticles typically function as substrates for immobilization of biomolecules, as catalytic agent, electron transfer agent between electrode surface and the biomolecules, and as reactants. Microfluidic deals with manipulating very small volumes of fluids (micro and nanoliters). This miniaturized platform enhances control of flow conditions and mixing rate of fluids. The microfluidics improves the sensitivity of the analysis, and reduces the volumes of sample and reagent in the analysis. The review specifically aims at representing microfluidics-based sensors and nanoparticle based sensors. This review will also focus on probable merger of these two fields to take advantage of both the fields and this will help in pushing the boundaries of these fields further more.

Functionalization of Gold Nanoparticles with Ru-Porphyrin and Their Selectivity in the Oligomerization of Alkynes

Gold nanoparticles (AuNPs) were functionalized by ruthenium porphyrins through a sulfur/gold covalent bond using a three-steps reaction. The catalyst was characterized by scanning electron microscopy (SEM) and thermogravimetric analysis (TGA) in order to control the binding of ruthenium porphyrin on AuNPs’ surface. The catalyst was tested and compared with an analog system not bound to AuNPs in the oligomerization reaction using 1-phenylacetylene as the substrate.

Rapid Detection and Antimicrobial Susceptibility Testing of Pathogens Using AgNPs-Invertase Complexes and the Personal Glucose Meter

Rapid detection of pathogens and assessment of antimicrobial susceptibility is of great importance for public health, especially in resource-limiting regions. Herein, we developed a rapid, portable, and universal detection method for bacteria using AgNPs-invertase complexes and the personal glucose meter (PGM). In the presence of bacteria, the invertase could be released from AgNPs-invertase complexes where its enzyme activity of invertase was inhibited. Then, the enzyme activity of invertase was restored and could convert sucrose into glucose measured by a commercially PGM. There was a good linear relationship between PGM signal and concentration of E. coli or S. aureus as the bacteria model with high sensitivity. And our proposed biosensor was proved to be a rapid and reliable method for antimicrobial susceptibility testing within 4 h with consistent results of Minimum Inhibitory Concentrations (MICs) testing, providing a portable and convenient method to treat infected patients with correct antibiotics and reduce the production of antibiotic-resistant bacteria, especially for resource-limiting settings.

Freshness monitoring of raw fish by detecting biogenic amines using a gold nanoparticle-based colorimetric sensor array

We herein report the quantitative detection of biogenic amines using a gold nanoparticle-based colorimetric chemosensor array for food analysis. The gold nanoparticles are functionalized with carboxylate derivatives, which capture target amines through hydrogen bonds and electrostatic interactions. The simultaneous discrimination of 10 amine derivatives was achieved by a linear discriminant analysis with a 100% correct classification based on the multi-colorimetric response pattern of structural differences. Furthermore, a real sample analysis for raw fish (i.e., tuna) demonstrated highly accurate determination of histamine concentrations by a support vector machine, the result of which was matched with high-performance liquid chromatography. Most importantly, the chemosensor array succeeded in detecting the time-dependent concentration change of histamine in the raw fish, meaning that the decomposition of the fish could be monitored by the colorimetric changes. Hence, the proposed chemosensor array combined with pattern recognition techniques can be a user-friendly analytical method for food freshness monitoring.

A gold nanoparticle-based chemosensor array functionalized with carboxylate derivatives performed freshness monitoring of amines in a fish sample.

Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring

Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various small-molecule metabolites.

Overview of the current status on emerging, multi-faceted nanosensor platform designs and data analysis strategies for rapid, point-of-need detection and monitoring of small-molecule metabolites.

Nanotechnology for Targeted Detection and Removal of Bacteria: Opportunities and Challenges

The emergence of nanotechnology has created unprecedented hopes for addressing several unmet industrial and clinical issues, including the growing threat so-termed "antibiotic resistance" in medicine. Over the last decade, nanotechnologies have demonstrated promising applications in the identification, discrimination, and removal of a wide range of pathogens. Here, recent insights into the field of bacterial nanotechnology are examined that can substantially improve the fundamental understanding of nanoparticle and bacteria interactions. A wide range of developed nanotechnology-based approaches for bacterial detection and removal together with biofilm eradication are summarized. The challenging effects of nanotechnologies on beneficial bacteria in the human body and environment and the mechanisms of bacterial resistance to nanotherapeutics are also reviewed.

Algae-mediated biosystems for metallic nanoparticle production: From synthetic mechanisms to aquatic environmental applications

Driven by the growing impetus of green chemistry and environmental protection, the use of bio-based systems to produce green metallic nanomaterials used for environmental remediation has thus developed urgently. It is proposed that using algae as a living cell factory or algal extract as a natural reducing agent is a green and clean way to efficiently synthesize various metallic nanomaterials. However, studies on algal-based biological synthesis of metallic nanomaterials and their applications towards removal of toxic pollutants from wastewater are still limited, which largely discourage the sustainability. Herein, this review aims to introduce the recent advances on algae-mediated nanomaterial-producing biosystems. The corresponding synthetic mechanisms, operation parameters, and case studies on various algae-synthesized metallic nanoparticles are comprehensively discussed and summarized. More importantly, the applicability of algae-synthesized metallic nanoparticles on water treatment is introduced in-depth. To improve economic viability, the challenges and future perspectives are also considered. Taken together, this review systematically presents the achievements and current progress of algae-mediated metallic nanoparticle biosynthesis towards the aquatic pollutants treatment, which can provide new insights on promoting the algae-based nanomaterial production yield and environmental application potential.

Recent progress in the optical detection of pathogenic bacteria based on noble metal nanoparticles

Pathogenic bacteria have become a huge threat to social health and economy for their frighteningly infectious and lethal capacity. It is quite important to make a diagnosis in advance to prevent infection or allow a rapid treatment after infection. Noble metal nanoparticles, due to their unique physicochemical properties, especially optical properties, have drawn a great attention during the past decades and have been widely applied into all kinds of fields related to human health. By utilizing these noble metal nanoparticles, optical diagnosis platforms towards pathogenic bacteria have emerged continually, providing highly sensitive, selective, and particularly facile detection tools for clinic or point-of-care diagnosis. This review summarizes the recent development in this field. It begins with a brief introduction of pathogenic bacteria and noble metal nanoparticles. And then, optical detection methods are systematically discussed in three distinct aspects. In addition to these proof-of-concept methods, corresponding algorithms and point-of-care detection devices are also described. Finally, the review ends up with subjective views on present limitations and some appropriate advice for future research directions.

Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L₂ regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.

Gold nanomaterials for optical biosensing and bioimaging

Gold nanoparticles (AuNPs) are highly compelling nanomaterials for biomedical studies due to their unique optical properties. By leveraging the versatile optical properties of different gold nanostructures, the performance of biosensing and biomedical imaging can be dramatically improved in terms of their sensitivity, specificity, speed, contrast, resolution and penetration depth. Here we review recent advances of optical biosensing and bioimaging techniques based on three major optical properties of AuNPs: surface plasmon resonance, surface enhanced Raman scattering and luminescence. We summarize the fabrication methods and optical properties of different types of AuNPs, highlight the emerging applications of these AuNPs for novel optical biosensors and biomedical imaging innovations, and discuss the future trends of AuNP-based optical biosensors and bioimaging as well as the challenges of implementing these techniques in preclinical and clinical investigations.

Array-Based Biosensors for Bacteria Detection: From the Perspective of Recognition

Array-based biosensors have shown as effective and powerful tools to distinguish intricate mixtures with infinitesimal differences among analytes such as nucleic acids, proteins, microorganisms, and other biomolecules. In array-based bacterial sensing, the recognition of bacteria is the initial step that can crucially influence the analytical performance of a biosensor array. Bacteria recognition as well as the signal readout and mathematical analysis are indispensable to ensure the discrimination ability of array-based biosensors. Strategies for bacteria recognition mainly include the specific interaction between biomolecules and the corresponding receptors on bacteria, the noncovalent interaction between materials and bacteria, and the specific targeting of bacterial metabolites. In this review, recent advances in array-based bacteria sensors are discussed from the perspective of bacteria recognition relying on the characteristics of different bacteria. Principles of bacteria recognition and signal readout for bacteria detection are highlighted as well as the discussion on future trends in array-based biosensors.

Gold Nanomaterials as a Promising Integrated Tool for Diagnosis and Treatment of Pathogenic Infections-A Review

This review summarizes research on functionalized gold nanomaterials as pathogen detection sensors and pathogen elimination integrated tools. After presenting the challenge of current severe threat from pathogenic bacteria and the increasingly serious growth rate of drug resistance, the first section mainly introduces the conspectus of gold nanostructures from synthesis, characterization, physicochemical properties and applications of gold nanomaterials. The next section deals with gold nanomaterials-based pathogen detection sensors such as colorimetric sensors, fluorescence sensors and Surface-Enhanced Raman Scattering sensors. We then discuss strategies based on gold nanomaterials for eliminating pathogenic infections, such as the dual sterilization strategy for grafting gold nanomaterials with antibacterial substances, photothermal antibacterial and photodynamic antibacterial methods. The fourth part briefly introduces the comprehensive strategy for diagnosis and sterilization of pathogen infection based on gold nanomaterials, such as the diagnosis and treatment strategy for pathogen infection using Roman signals real-time monitoring and photothermal sterilization. A concluding section that summarizes the current status and challenges of the novel diagnosis and treatment integrated strategy for pathogenic infections, gives an outlook on potential future perspectives.

Array-based microbial identification upon extracellular aminoglycoside residue sensing

Sensitive and rapid identification of pathogenic microorganisms is of great importance for clinical diagnosis and treatment. In this study, we developed an ultrasensitive colorimetric sensor array (CSA) based on the interactions between aminoglycoside antibiotics (AMGs) and Ag nanoparticles decorated with β-cyclodextrin (AgNPs@β-CD) to discriminate microorganisms quickly and accurately. Microorganisms can absorb different amounts of AMGs after incubation. Upon the addition of AgNPs@β-CD, the corresponding extracellular AMG residues will bind to AgNPs@β-CD, leading to color changes due to the modifications in localized surface plasmon resonance. The array was developed using 4 AMGs as sensing elements and AgNPs@β-CD as the colorimetric probe to generate a unique colorimetric response pattern for each microorganism. Standard chemometric methods indicated excellent discrimination among 20 microorganisms at low concentrations of 2 × 10⁶ CFU/mL. Therefore, this ultrasensitive CSA can be used for microbial discrimination portably and efficiently. Importantly, the concentration of microbial discrimination by our array is much lower than that of prior CSAs. This method of extracellular residue sensing also provided a new strategy to improve the sensitivity of conventional CSA in the discrimination of microorganisms, to measure the amount of intercellular uptake of AMGs by microorganisms, and to screen drugs that can easily be accumulated by the pathogenic microorganisms.

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.

The universal dual-mode aptasensor for simultaneous determination of different bacteria based on naked eyes and microfluidic-chip together with magnetic DNA encoded probes

It was critically important to develop some sensitive, convenient and on-site methods for simultaneous assay of different pathogenic bacteria in foods. In this work, a dual-mode aptasensor was established for fulfilling above aims combing colorimetry with microfluidic chip. This as-prepared dual-mode aptasensor not only realized rapid screening by naked eye on-site, but also the simultaneous quantification of multiple bacteria. Namely, the presence of pathogenic bacteria was firstly judged by naked eyes with Salmonella typhimurium (S.T) and Vibrio parahaemolyticus (V.P) as models. And then, S.T and V.P in positive samples were simultaneously quantified by microfluidic chip. In order to obtain the multiple signals, a series of magnetic DNA encoded-probes (MDEs) was fabricated containing rolling cycle amplified long DNA chain (RCA-DNA) rich in G-quadruplex sequences. They can combine with hemin as DNAzyme to catalyze 3,3'-5,5'-Tetramethyl benzidine (TMB)-H₂O₂ system for color development and be cleaved by EcoRV endonuclease to produce DNA fragments with different lengths. The microfluidic chip was employed to separate and quantify the fragments for quantifying S.T and V.P simultaneously. For this protocol, 100 CFU·mL^-1 of V.P or S.T could be observed by the naked eye and as low as 32 S.T and 30 CFU·mL^-1 V.P could be detected by the chip within 3 min. The dual-mode aptasensor could quickly screen positive samples, and simultaneously perform quantitative detection of the bacteria in positive samples. Our protocol demonstrated its potential in on-site qualification & simultaneous quantification of foodborne bacteria in foods.

A low pH-based rapid and direct colorimetric sensing of bacteria using unmodified gold nanoparticles

Herein we report a novel strategy for the detection of bacteria using unfunctionalized gold nanoparticles (AuNPs), which was utilized as a colorimetric sensor. The UV-vis absorbance of AuNPs showed red shift due to the interactions between bacteria and AuNPs in the high acidic environment, producing a distinct color change which can be visually detected by naked-eye. The proposed low pH-based colorimetric assay was studied with seven types of foodborne bacteria, and the detection limit was found to be 1.6 × 10⁷ CFU/mL, 3.3 × 10⁵ CFU/mL, 4.5 × 10⁶ CFU/mL, 5.8 × 10⁶ CFU/mL, 2.8 × 10⁵ CFU/mL, 4.4 × 10⁷ CFU/mL and 6.6 × 10⁶ CFU/mL for strains Staphylococcus aureus, Shigella flexneri, Pseudomonas aeruginosa, Vibrio parahaemolyticus, Bacillus subtilis, Escherichia coli O157:H7, and Salmonella typhimurium respectively. The result can be observed by naked-eye within 5 min, the color changed from red to purple, blue and colorless with increasing the concentration of the bacteria, indicated the assay have the ability to differentiate bacteria of different concentrations. This work demonstrates that low pH-based colorimetric assay using unfunctionalized AuNPs for the directly detection of untreated bacteria is fast, simple and visual, has the potential for applications in bacterial diagnostics, especially the detection of pathogenic bacteria in food.