Computational methods for the analysis of chemical sensor array data from volatile analytes

Comparative Analysis of Machine Learning Algorithms Used for Translating Aptamer-Antigen Binding Kinetic Profiles to Diagnostic Decisions

Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.6 Hz) impedance data during the binding of viral protein targets to trimeric aptamers. The impedance data collected from 172 COVID-19 saliva samples were processed through multiple nonlinear regression models to extract nine key features from the transient signals. These features were then used to train three supervised ML algorithms─support vector machine (SVM), artificial neural network (ANN), and random forest (RF)─using a 75:25 training-testing ratio. Traditional ROC-based classification achieved an accuracy of 83.6%, while ML-based models significantly improved performance, with SVM, ANN, and RF achieving accuracies of 86.0%, 100%, and 100%, respectively. The ANN model demonstrated superior performance in handling complex and high-variance biosensor data, providing a robust and scalable solution for improving diagnostic accuracy in point-of-care settings.

A simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells

The intracellular levels of biothiols are associated with various diseases including cancer, and biothiols are regarded as tumor biomarker. Due to the similarity of the molecular structure of biothiols, the development of simple, rapid, efficient, and sensitive colorimetric sensor arrays holds great promise for clinical cancer diagnosis. Here, we developed a simple Ag-MoS2 hybrid nanozyme-based sensor array for colorimetric identification of biothiols and cancer cells. The novel Ag-MoS2 nanoprobe was synthesized in a simple and efficient way through the in situ self-reduction reaction between MoS2 and noble metal precursor. Benefiting from to the formation of heterogeneous metal structures, the peroxidase (POD)-like catalytic activity of the synthesized Ag-MoS2 hybrid nanocomposites is significantly enhanced compared to MoS2 alone. Moreover, the catalytic activity of Ag-MoS2 nanozyme was correlated with the pH of the reaction solution and the inhibitory effects of the three biothiols on the nanozyme-triggered chromogenic system differed in the specific pH environments. Therefore, each sensing unit of this electronic tongue generated differential colorimetric fingerprints of different biothiols. After principal component analysis (PCA), the developed novel colorimetric sensor array can accurately discriminate biothiols between different types, various concentrations, and different mixture proportions. Further, the sensor array was used for the colorimetric identification of real serum and cellular samples, demonstrating its great potential in tumor diagnostic applications.

Multiplex Profiling of miR-122 for Preclinical and Clinical Evaluation of Drug-Induced Liver Injury by a Full-Scale Platform

Diagnostic and monitoring for drug-induced liver injury (DILI) predominantly rely on serum aminotransferases. However, owing to their widespread expression across multiple organs, a significant challenge emerges from the absence of reliable biomarkers for DILI diagnosis. Herein, we introduce a concept for DILI detection, circumventing the nonspecific elevation and delayed release of aminotransferases and then straightforwardly focusing on the core feature of DILI, abnormal gene expression caused by drug overdose. The developed full-scale platform integrates the properties of spherical nucleic acids with elaborately designed fluorescence in situ hybridization sequences, enabling the sensitive and specific profiling of drug-overdosed miR-122 expression alterations across molecular, cellular, organismal, and clinical scales and effectively bypassing the phenotypic features of disease. Furthermore, the diagnostic efficacies of serum and total RNA extracted from both mouse and human blood samples for DILI diagnosis were analyzed using the receiver operating characteristic curve and principal component analysis. We anticipate that this universal platform holds potential in facilitating DILI diagnosis, therapeutic evaluation, and prognosis.

DNA Framework-Based Programmable Atom-Like Nanoparticles for Non-Coding RNA Recognition and Differentiation of Cancer Cells

The cooperative diagnosis of non-coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework-based programmable atom-like nanoparticles (PANs) are designed to develop molecular classifiers for intra-cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs-based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high-fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non-amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs-based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.

Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Ultrasensitive determination of surface proteins on tumor-derived small extracellular vesicles for breast cancer identification based on lanthanide-activated signal amplification strategy

Small extracellular vesicles (sEVs) carrying multiple tumor-associated proteins inherited from parental cells play crucial roles in noninvasive breast cancer (BC) diagnosis. However, it is challenging to assess the subtle variations of surface proteins on sEV membranes due to the highly heterogeneous BC. Therefore, a simple and ultrasensitive assay based on lanthanide (Ln3+)-activated luminescence signal amplification was developed to detect multiple surface proteins on BC-derived sEVs. Multiple protein biomarkers on sEVs can be well identified with high sensitivity and specificity through dissolution-amplified luminescence of the NaEuF4 nanoparticle-based nanoprobe. We employ linear discriminant analysis to successfully discriminate triple negative BC cell (MDA-MB-231 cell) derived sEVs from other breast cell lines (MCF-7, SK-BR-3, BT474 and MCF-10A cell). Furthermore, the strategy enables high accuracy for districting the progression stages of BC patients and healthy donors. The simple and sensitive signal amplification strategy exhibits great potential for early clinic diagnosis by precise protein profiling of sEVs.

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors

Recently, there has been growing concern about the discharge of water-soluble polymers (especially synthetic polymers) into the environment. Therefore, the identification of water-soluble polymers in water samples is becoming increasingly crucial. In this study, a chemical tongue system to simply and precisely identify water-soluble polymers using multiple fluorescently responsive peptide sensors was demonstrated. Fluorescence spectra obtained from the mixture of each peptide sensor and water-soluble polymer were changed depending on the combination of the polymer species and peptide sensors. Water-soluble polymers were successfully identified through the supervised or unsupervised machine learning of multidimensional fluorescence signals from the peptide sensors.

Enantioselective and Chemoselective Optical Detection of Chiral Organic Compounds without Resorting to Chromatography

Enantiorecognition and resolution are of essential importance in many diverse areas of science. Whenever there arises a need to analyze/investigate enantiomers in different situations chromatography stands up in our minds immediately. Nevertheless, chemoselective and enantioselective recognition/discrimination (without going for separation) constitutes a different perception and requirement. The techniques using chiroptical sensing cause detection based on molecular interactions induced in different manners. Enantioselective sensing of monosaccharides in γ-cyclodextrin assembly and by diboronic acid based fluorescent sensors, application of bi-naphthol and H8 BINOL based sensors and dendrimers, metal-to-ligand charge transfer transitions in CD, exciton-coupled circular dichroism, surface enhanced Raman spectroscopy, and enantioselective indicator displacement sensor arrays for enantioselective recognition/detection of chiral organic compounds, such as amines, amino acids/alcohols, and hydroxycarboxylic acids have been discussed in progressive manner with mechanistic explanations, wherever available. Besides, the chiroptical vs LC approach has been discussed. The present paper is focused on certain different non-chromatographic optical techniques and aims to extend an understanding and a view to consider such techniques which have been successful in selective detection, and determination of absolute configuration and enantiomeric excess, (without resorting to separation vis-à-vis LC) and that have potential use in high-throughput chiral assay and combinatorial search for asymmetric catalysts and reagents.

Peroxidase-Like FeCoZn Triple-Atom Catalyst-Based Electronic Tongue for Colorimetric Discrimination of Food Preservatives

Recently, single-atom catalysts are attracting much attention in sensor field due to their remarkable peroxidase- or oxidase-like activities. Herein, peroxidase-like FeCoZn triple-atom catalyst supported on S- and N-doped carbon derived from ZIF-8 (FeCoZn-TAC/SNC) serves as a proof-of-concept nanozyme. In this paper, a dual-channel nanozyme-based colorimetric sensor array is presented for identifying seven preservatives in food. Further experiments reveal that the peroxidase-like activity of the FeCoZn TAzyme enables it to catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) and o-phenylenediamine (OPD) in the presence of H₂ O₂ , yielding the blue oxTMB and yellow oxOPD, respectively. However, food preservatives are adsorbed on the nanozyme surface through π-π stacking interaction and hydrogen bond, and the reduction in catalytic activity of FeCoZn TAzyme causes differential colorimetric signal variations, which provide unique "fingerprints" for each food preservative.

Printed colorimetric chemosensor array on a 96-microwell paper substrate for metal ions in river water

Here, we propose a printed 96-well microtiter paper-based chemosensor array device (PCSAD) to simultaneously detect metal ions for river water assessment. Colorimetric chemosensors for metal ions have been designed based on molecular self-assembly using off-the-shelf catechol dyes and a phenylboronic acid (PBA) derivative. The colorimetric self-assembled chemosensors consisting of catechol dyes and a PBA derivative on a 96-well microtiter paper substrate demonstrated various color changes according to the disassembly of the ensembles by the addition of nine types of metal ions. An in-house-made algorithm was used to automate imaging analysis and extract color intensities at seven types of color channels from a captured digital image, allowing for rapid data processing. The obtained information-rich inset data showed fingerprint-like colorimetric responses and was applied to the qualitative and quantitative pattern recognition of metal ions using chemometric techniques. The feasibility of the 96-well microtiter PCSAD for environmental assessment has been revealed by the demonstration of a spike-and-recovery test against metal ions in a river water sample.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

The translational paradigm of nanobiomaterials: Biological chemistry to modern applications

Recently nanotechnology has evolved as one of the most revolutionary technologies in the world. It has now become a multi-trillion-dollar business that covers the production of physical, chemical, and biological systems at scales ranging from atomic and molecular levels to a wide range of industrial applications, such as electronics, medicine, and cosmetics. Nanobiomaterials synthesis are promising approaches produced from various biological elements be it plants, bacteria, peptides, nucleic acids, etc. Owing to the better biocompatibility and biological approach of synthesis, they have gained immense attention in the biomedical field. Moreover, due to their scaled-down sized property, nanobiomaterials exhibit remarkable features which make them the potential candidate for different domains of tissue engineering, materials science, pharmacology, biosensors, etc. Miscellaneous characterization techniques have been utilized for the characterization of nanobiomaterials. Currently, the commercial transition of nanotechnology from the research level to the industrial level in the form of nano-scaffolds, implants, and biosensors is stimulating the whole biomedical field starting from bio-mimetic nacres to 3D printing, multiple nanofibers like silk fibers functionalizing as drug delivery systems and in cancer therapy. The contribution of single quantum dot nanoparticles in biological tagging typically in the discipline of genomics and proteomics is noteworthy. This review focuses on the diverse emerging applications of Nanobiomaterials and their mechanistic advancements owing to their physiochemical properties leading to the growth of industries on different biomedical measures. Alongside the implementation of such nanobiomaterials in several drug and gene delivery approaches, optical coding, photodynamic cancer therapy, and vapor sensing have been elaborately discussed in this review. Different parameters based on current challenges and future perspectives are also discussed here.

Modular Assembly of MXene Frameworks for Noninvasive Disease Diagnosis via Urinary Volatiles

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laser-induced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plug-and-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.

Cucurbit[7]uril Macrocyclic Sensors for Optical Fingerprinting: Predicting Protein Structural Changes to Identifying Disease-Specific Amyloid Assemblies

In a three-dimensional (3D) representation, each protein molecule displays a specific pattern of chemical and topological features, which are altered during its misfolding and aggregation pathway. Generating a recognizable fingerprint from such features could provide an enticing approach not only to identify these biomolecules but also to gain clues regarding their folding state and the occurrence of pathologically lethal misfolded aggregates. We report here a universal strategy to generate a fluorescent fingerprint from biomolecules by employing the pan-selective molecular recognition feature of a cucurbit[7]uril (CB[7]) macrocyclic receptor. We implemented a direct sensing strategy by covalently tethering CB[7] with a library of fluorescent reporters. When CB[7] recognizes the chemical and geometrical features of a biomolecule, it brings the tethered fluorophore into the vicinity, concomitantly reporting the nature of its binding microenvironment through a change in their optical signature. The photophysical properties of the fluorophores allow a multitude of probing modes, while their structural features provide additional binding diversity, generating a distinct fluorescence fingerprint from the biomolecule. We first used this strategy to rapidly discriminate a diverse range of protein analytes. The macrocyclic sensor was then applied to probe conformational changes in the protein structure and identify the formation of oligomeric and fibrillar species from misfolded proteins. Notably, the sensor system allowed us to differentiate between different self-assembled forms of the disease-specific amyloid-β (Aβ) aggregates and segregated them from other generic amyloid structures with a 100% identification accuracy. Ultimately, this sensor system predicted clinically relevant changes by fingerprinting serum samples from a cohort of pregnant women.

Multiple-Aptamer-Integrated DNA-Origami-Based Chemical Nose Sensors for Accurate Identification of Cancer Cells

Developing simple, rapid, and accurate methods for cancer cell identification could facilitate early cancer diagnosis and tumor metastasis research. Herein, we develop a novel chemical nose sensor that employs the collective recognition abilities of a set of multiple-aptamer-integrated DNA origami (MADO) probes for discriminative identification of cancer cells. By controlling the types and/or copies of aptamers assembled on the DNA origami nanostructure, we constructed five MADO probes with differential binding affinities (ranging from 3.08 to 78.92 nM) to five types of cells (HeLa, MDA-MB-468, MCF-7, HepG2, and MCF-10A). We demonstrate the utility of the MADO-based chemical nose sensor in the identification of blinded unknown cell samples with a 95% accuracy. This sensing platform holds great potential for applications in medical diagnostics.

Single-Molecule Mechanochemical Sensing

Single-molecule mechanochemical sensing (SMMS) is a novel biosensing technique using mechanical force as a signal transduction mechanism. In the mechanochemical sensing, the chemical binding of an analyte molecule to a sensing template is converted to mechanical signals, such as tensile force, of the template. Since mechanical force can be conveniently monitored by single-molecule tools, such as optical tweezers, magnetic tweezers, or Atomic Force Microscopy, mechanochemical sensing is often carried out at the single molecule level. In traditional format of ensemble sensing, sensitivity can be achieved via chemical or electrical amplifications, which are materials intensive and time-consuming. To address these problems, in 2011, we used the principle of mechanochemical coupling in a single molecular template to detect single nucleotide polymorphism (SNP) in DNA fragments. The single-molecule sensitivity in such SMMS strategy allows to removing complex amplification steps, drastically conserving materials and increasing temporal resolution in the sensing. By placing many probing units throughout a single-molecule sensing template, SMMS can have orders of magnitude better efficiency in the materials usage (i.e., high Atom Economy) with respect to the ensemble biosensing. The SMMS sensing probes also enable topochemical arrangement of different sensing units. By placing these units in a spatiotemporally addressable fashion, single-molecule topochemical sensors have been demonstrated in our lab to detect an expandable set of microRNA targets. Because of the stochastic behavior of single-molecule binding, however, it is challenging for the SMMS to accurately report analyte concentrations in a fixed time window. While multivariate analysis has been shown to rectify background noise due to stochastic nature of single-molecule probes, a template containing an array of sensing units has shown ensemble average behaviors to address the same problem. In this so-called ensemble single-molecule sensing, collective mechanical transitions of many sensing units occur in the SMMS sensing probes, which allows accurate quantification of analytes. For the SMMS to function as a viable sensing approach readily adopted by biosensing communities, the future of the SMMS technique relies on the reduction in the complexity and cost of instrumentation to report mechanical signals. In this account, we first explain the mechanism and main features of the SMMS. We then specify basic elements employed in SMMS. Using DNA as an exemplary SMMS template, we further summarize different types of SMMS which present multiplexing capability and increased throughput. Finally, recent efforts to develop simple and affordable high throughput methods for force generation and measurement are discussed in this Account for potential usage in the mechanochemical sensing.

A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds

Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds have been developed in recent years. In particular, approaches combining machine learning technology enable precisely recognizing the MoA of antibacterial compounds. However, these methods heavily rely on the big data resulting from multiplexed experiments. As such, these approaches tend to produce minimal throughput and are not comprehensive enough to be adapted to widespread industrial applications. Here, we present a machine learning approach based on a customized array sensor for directly identifying the MoAs of antibacterial compounds. The array sensor consists of different two-dimensional nanomaterial fluorescence quenchers with different fluorescence-labeled single-stranded DNAs (ssDNAs). By mapping the subtle difference of the physicochemical properties on the bacterial surface treated with different antibacterial compound stimuli, the array sensor ensures visualizing the recognition process. Moreover, the customized array sensor produces a high volume of the MoA database, overcoming the dependence on big data. We further use the array sensor to build a chemical-response unique "fingerprint" database of MoAs. By combining a neural network-based genetic algorithm (NNGA), we rapidly discriminate the MoAs of four antibiotics with an overall accuracy of 100%. Furthermore, a new screening antibacterial peptide has been discovered and evaluated by our approach for determining the MoA with high accuracy proven by other techniques.

Single Probe-Based Chemical-Tongue Sensor Array for Multiple Bacterial Identification and Photothermal Sterilization in Real Time

Simple and efficient identification of multiple bacteria and sterilization in real time is of considerable significance for clinical diagnostics and quality control in food. Herein, a novel chemical-tongue sensor array with 3,3',5,5'-tetramethylbenzidine (TMB) as a single probe was developed for bacterial identification and photothermal elimination. The synthesized bimetallic palladium/platinum nanoparticles (Pd/Pt_NPs) present excellent catalytic capability that can catalyze TMB into oxidized TMB (oxTMB) with four feature absorption peaks. Bacteria have different ability on inhibiting the reaction between TMB and Pd/Pt_NPs. With the absorbance intensity of oxTMB at the four feature peaks as readout, nine kinds of bacteria including two drug-resistant bacteria can be successfully distinguished via linear discriminant analysis. Remarkably, oxTMB exhibits excellent photothermal properties and can effectively kill bacteria in real time under near-infrared laser irradiation. The strategy of selecting TMB as a single probe simplifies the experimental operation and reduces the time cost. Furthermore, the developed sensing system was used to promote the wound healing process of MRSA-infected mice in vivo. The investigation provides a promising simple and efficient strategy for bacterial identification and sterilization with a universal platform, which has great potential application in clinical diagnosis and therapy.

Enantioselective sensing of insect pheromones in water

An arrayed combination of water-soluble deep cavitands and cationic dyes has been shown to optically sense insect pheromones at micromolar concentration in water. Machine learning approaches were used to optimize the most effective array components, which allows differentiation between small structural differences in targets, including between different diastereomers, even though the pheromones have no innate chromophore. When combined with chiral additives, enantiodiscrimination is possible, dependent on the size and shape of the pheromone.

Identification of Water-Soluble Polymers through Discrimination of Multiple Optical Signals from a Single Peptide Sensor

The pollution of water environments is a worldwide concern. Not only marine pollution by plastic litter, including microplastics, but also the spillage of water-soluble synthetic polymers in wastewater have recently gained increasing attention due to their potential risks to soil and water environments. However, conventional methods to identify polymers dissolved in water are laborious and time-consuming. Here, we propose a simple approach to identify synthetic polymers dissolved in water using a peptide-based molecular sensor with a fluorophore unit. Supervised machine learning of multiple fluorescence signals from the sensor, which specifically or nonspecifically interacted with the polymers, was applied for polymer classification as a proof of principle demonstration. Aqueous solutions containing different polymers or multiple polymer species with different mixture ratios were identified successfully. We found that fluorophore-introduced biomolecular sensors have great potential to provide discriminative information regarding water-soluble polymers. Our approach based on the discrimination of multiple optical signals of water-soluble polymers from peptide-based molecular sensors through machine learning will be applicable to next-generation sensing systems for polymers in wastewater or natural environments.

A perception-based nanosensor platform to detect cancer biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

Non-injective gas sensor arrays: identifying undetectable composition changes

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH₄/N₂/CO₂/C₂H₆mixtures relevant to natural gas sensing.

A brief insight to the role of glyconanotechnology in modern day diagnostics and therapeutics

Carbohydrate-protein and carbohydrate-carbohydrate interactions are very important for various biological processes. Although the magnitude of these interactions is low compared to that of protein-protein interaction, the magnitude can be boosted by multivalent approach known as glycocluster effect. Nanoparticle platform is one of the best ways to present diverse glycoforms in multivalent manner and thus, the field of glyconanotechnology has emerged as an important field of research considering their potential applications in diagnostics and therapeutics. Considerable advances in the field have been achieved through development of novel techniques, use of diverse metallic and non-metallic cores for better efficacy and application of ever-increasing number of carbohydrate ligands for site-specific interaction. The present review encompasses the recent developments in the area of glyconanotechnology and their future promise as diagnostic and therapeutic tools.

Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System

An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting selective molecular recognition. The sensor is highly selective and can discriminate between folds as similar as native G-quadruplexes and those with bulges or vacancies. The host and guest can form heteroternary complexes with DNA strands, with the host acting as mediator between the DNA and dye, modulating the emission. By applying machine learning algorithms to the sensing data, prediction of the folding state of unknown DNA strands is possible with high fidelity.

The UV Effect on the Chemiresistive Response of ZnO Nanostructures to Isopropanol and Benzene at PPM Concentrations in Mixture with Dry and Wet Air

Towards the development of low-power miniature gas detectors, there is a high interest in the research of light-activated metal oxide gas sensors capable to operate at room temperature (RT). Herein, we study ZnO nanostructures grown by the electrochemical deposition method over Si/SiO2 substrates equipped by multiple Pt electrodes to serve as on-chip gas monitors and thoroughly estimate its chemiresistive performance upon exposing to two model VOCs, isopropanol and benzene, in a wide operating temperature range, from RT to 350 °C, and LED-powered UV illumination, 380 nm wavelength; the dry air and humid-enriched, 50 rel. %, air are employed as a background. We show that the UV activation allows one to get a distinctive chemiresistive signal of the ZnO sensor to isopropanol at RT regardless of the interfering presence of H2O vapors. On the contrary, the benzene vapors do not react with UV-illuminated ZnO at RT under dry air while the humidity’s appearance gives an opportunity to detect this gas. Still, both VOCs are well detected by the ZnO sensor under heating at a 200–350 °C range independently on additional UV exciting. We employ quantum chemical calculations to explain the differences between these two VOCs’ interactions with ZnO surface by a remarkable distinction of the binding energies characterizing single molecules, which is −0.44 eV in the case of isopropanol and −3.67 eV in the case of benzene. The full covering of a ZnO supercell by H2O molecules taken for the effect’s estimation shifts the binding energies to −0.50 eV and −0.72 eV, respectively. This theory insight supports the experimental observation that benzene could not react with ZnO surface at RT under employed LED UV without humidity’s presence, indifference to isopropanol.

Machine Learning-Assisted Array-Based Detection of Proteins in Serum Using Functionalized MoS2 Nanosheets and Green Fluorescent Protein Conjugates

Abnormal concentrations of a specific protein or the presence of some biomarker proteins may indicate life-threatening diseases. Pattern-based detection of specific analytes using affinity-regulated receptors is one of the potential alternatives to specific antigen-antibody-based detection. In this report, we have schemed a sensor array by using various functionalized two-dimensional (2D)-MoS2 nanosheets and green fluorescent protein (GFP) as the receptor and the signal transducer, respectively. Two-dimensional MoS2 has been used as a promising candidate for recognition of the bioanalytes because of its high surface-to-volume ratio compared to those of other nanomaterials. Easy surface tunability of this material provides additional advantages to analyze the target of interest. The optimized 2D-MoS2-GFP conjugates are able to discriminate 15 different proteins at 50 nM concentration with a detection limit of 1 nM. Moreover, proteins in the binary mixture and in the presence of serum were discriminated successfully. Ten different proteins in serum media at relevant concentrations were classified successfully with 100% jackknifed classification accuracy, which proves the potentiality of the above system. We have also implemented and discussed the implication of using different machine learning models on the pattern recognition problem associated with array-based sensing.

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.

A chemiresistive methane sensor

A chemiresistive sensor is described for the detection of methane (CH₄), a potent greenhouse gas that also poses an explosion hazard in air. The chemiresistor allows for the low-power, low-cost, and distributed sensing of CH₄ at room temperature in air with environmental implications for gas leak detection in homes, production facilities, and pipelines. Specifically, the chemiresistors are based on single-walled carbon nanotubes (SWCNTs) noncovalently functionalized with poly(4-vinylpyridine) (P4VP) that enables the incorporation of a platinum-polyoxometalate (Pt-POM) CH₄ oxidation precatalyst into the sensor by P4VP coordination. The resulting SWCNT-P4VP-Pt-POM composite showed ppm-level sensitivity to CH₄ and good stability to air as well as time, wherein the generation of a high-valent platinum intermediate during CH₄ oxidation is proposed as the origin of the observed chemiresistive response. The chemiresistor was found to exhibit selectivity for CH₄ over heavier hydrocarbons such as n-hexane, benzene, toluene, and o-xylene, as well as gases, including carbon dioxide and hydrogen. The utility of the sensor in detecting CH₄ using a simple handheld multimeter was also demonstrated.

Multiclass spectral feature scaling method for dimensionality reduction

Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that are used to modify the features. Specifically, we reformulate the Laplacian eigenproblem of the spectral clustering as an eigenproblem of a linear matrix pencil whose eigenvector has the scaling factors. Numerical experiments show that the proposed method outperforms well-established supervised dimensionality reduction methods for toy problems with more samples than features and real-world problems with more features than samples.

High-Performance Colorimetric Humidity Sensors Based on Konjac Glucomannan

High-humidity conditions (85-100% relative humidity (RH)) have very diverse effects on many aspects of people's daily lives. Despite remarkable progress in the development of structural coloration-based humidity sensors, how to significantly improve the sensitivity and visual humidity resolution of these humidity sensors under a high-humidity environment remains a great challenge. In this study, high-performance colorimetric humidity sensors based on environment-friendly konjac glucomannan (KGM) via thin-film interference are developed using a simple, affordable, and scalable preparation method. An effective strategy is demonstrated for substantially improving the sensor sensitivity and visual humidity resolution under a high-humidity environment via synergistic integration of multiorder interference peaks, sensor array technology, and superior water-absorbing polymer. The KGM full-range humidity sensors exhibit fast and dynamic response toward the humidity change without power consumption, and they also show high sensitivity and selectivity, little hysteresis, and excellent stability against high-humidity conditions. The KGM humidity sensors display extraordinary red shift of the reflection peak (e.g., 385 nm) and the visual humidity resolution as high as 1.5% RH in the visible range from 85 to 100% RH, which represent the largest spectra shift and highest visual humidity resolution, respectively, for structural coloration-based humidity sensors in high-humidity conditions.

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.

Protein discrimination based on DNA induced perylene probe self-assembly

The development of a simple and effective method for the highly sensitive and selective discrimination of proteins is a subject of enormous interest. Herein, we report the construction of a novel fluorescence detection method based on a perylene probe for the highly efficient discrimination of multiple proteins. Single-stranded DNA (ssDNA) could induce aggregation of the perylene probe which caused quenching of probe fluorescence. After the addition of a protein, the protein could interact with the ssDNA-probe assembly complex with "turn-on" or further "turn-off" fluorescence response. A sensor array was designed based on the above phenomena which could realize the successful discrimination of proteins with 100% accuracy of cross validation. Nine representative proteins were successfully recognized. Moreover, it was observed that a protein could induce characteristic effect on the DNA-probe assembly with varying pH of assay buffer. Thus, different proteins showed unique fluorescence response towards assay buffers having different pH values. The assay buffer pH was then utilized as a sensing channel. Based on Linear Discriminant Analysis (LDA) nine proteins were successfully discriminated at the nanomolar concentration with 100% accuracy of cross validation. Furthermore, the sensor array also demonstrated differentiation of the nine proteins regardless of their concentration. The developed sensor array could also detect the proteins with great precision in human urine sample at a quite low concentration, which suggests its practical applicability for analysis of biological fluids.

A colorimetric sensor array for the discrimination of Chinese liquors

Although some colorimetric sensor arrays have been developed for the identification of Chinese liquors, they usually require the confirmation of volatile markers in the liquors by chromatography and mass spectrometry firstly. Herein, we present a simple colorimetric sensor array to identify various Chinese liquors in the liquid phase without the aid of other analytical techniques. The colorimetric sensor array consists of six commercially available and inexpensive solvatochromic dyes, and the sensing mechanism of this array is based on the response of solvatochromic dyes to their local polarity. On the basis of the colour changes of the sensor array, different Chinese liquors are discerned readily using pattern recognition methods, and the statistical analysis results (i.e., hierarchical clustering analysis and principal component analysis) reveal that the as-fabricated sensor array can distinguish the subtle differences between different liquors from the same winery and the same flavor type. Moreover, the developed sensor array can even distinguish diverse diluted liquors from the pristine ones.

H2-gas sensing and discriminating actions of a single-yarn sensor based on a Pd/GO multilayered thin film using FFT

A single-yarn H2-gas sensor was fabricated by self-assembling poly(allylamine hydrochloride) (PAH), poly(styrenesulfonic acid) sodium salt (PSS), graphene oxide (GO) and Pd-based complex thin films layer-by-layer on a single-yarn and then in situ reducing the Pd-based complex to a Pd/GO/PAH/PSS/PAH multilayered thin film. The H2-gas sensing properties, effect of bending and humidity influence on this sensor were investigated. The sensor exhibited a high response and good linearity over the range of 1000 to 10 000 ppm of H2 gas. The response of the sensor decreased under both conditions of a bending angle up to 20° and ambient humidity above 50% RH. A fast Fourier transform (FFT) analyzer was employed to disperse the signals of the sensor under the conditions of bending and ambient humidity influence in the presence of H2 gas. Differentiation of the amplitude of FFT from the first-order to second-order frequency spectra effectively increased the discrimination capability of the sensor under the conditions of bending and humidity influence.

Cascade Reaction-Based Chemiresistive Array for Ethylene Sensing

Chemiresistive sensors, which are based on semiconducting materials, offer real-time monitoring of environment. However, detection of nonpolar chemical substances is often challenging because of the weakness of the doping effect. Herein, we report a concept of combining a cascade reaction (CR) and a chemiresistive sensor array for sensitive and selective detection of a target analyte (herein, ethylene in air). Ethylene was converted to acetaldehyde through a Pd-catalyzed heterogeneous Wacker reaction at 40 °C, followed by condensation with hydroxylamine hydrochloride to emit HCl vapor. HCl works as a strong dopant for single-walled carbon nanotubes (SWCNTs), enabling the main sensor to detect ethylene with excellent sensitivity (10.9% ppm^-1) and limit of detection (0.2 ppm) in 5 min. False responses that occur in the main sensor are easily discriminated by reference sensors that partially employ CR. Moreover, though the sensor monitors the variation of normalized electric resistance (ΔR/R₀) in the SWCNT network, temporary deactivation of CR yields a sensor system that does not require analyte-free air for a baseline correction (i.e., estimation of R₀) and recovery of response. The concept presented here is generally applicable and offers a solution for several issues that are inherently present in chemiresistive sensing systems.

Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks

Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol.

Curating Metal-Organic Frameworks To Compose Robust Gas Sensor Arrays in Dilute Conditions

Metal-organic frameworks (MOFs), tunable, nanoporous materials, are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from gas composition space to sensor array response space defined by the matrix H of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of H is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO₂ and SO₂ in the gas phase.

Vapochromic crystals: understanding vapochromism from the perspective of crystal engineering

Vapochromic materials, which undergo colour and/or emission changes upon exposure to certain vapours or gases, have received increasing attention recently because of their wide range of applications in, e.g., chemical sensors, light-emitting diodes, and environmental monitors. Vapochromic crystals, as a specific kind of vapochromic materials, can be investigated from the perspective of crystal engineering to understand the mechanism of vapochromism. Moreover, understanding the vapochromism mechanism will be beneficial to design and prepare task-specific vapochromic crystals as one kind of low-cost 'electronic nose' to detect toxic gases or volatile organic compounds. This review provides important information in a broad scientific context to develop new vapochromic materials, which covers organometallic or coordination complexes and organic crystals, as well as the different mechanisms of the related vapochromic behaviour. In addition, recent examples of supramolecular vapochromic crystals and metal-organic-framework (MOFs) vapochromic crystals are introduced.

Selective sensing of THC and related metabolites in biofluids by host:guest arrays

A host–guest fluorescence sensor array can selectively detect THC and its metabolites in biofluids such as urine and saliva.

Multichannel DNA Sensor Array Fingerprints Cell States and Identifies Pharmacological Effectors of Catabolic Processes

Cells at disease onset are often associated with subtle changes in the expression level of a single or few molecular components, making traditionally used biomarker-driven clinical diagnosis a challenging task. We demonstrate here the design of a DNA nanosensor array with multichannel output that identifies the normal or pathological state of a cell based on the alteration of its global proteomic signature. Fluorophore-encoded single-stranded DNA (ssDNA) strands were coupled via supramolecular interaction with a surface-functionalized gold nanoparticle quencher to generate this integrated sensor array. In this design, ssDNA sequences exhibit dual roles, where they provide differential affinities with the receptor gold nanoparticle as well as act as transducer elements. The unique interaction mode of the analyte molecules disrupts the noncovalent supramolecular complexation, generating simultaneous multichannel fluorescence output to enable signature-based analyte identification via a linear discriminant analysis-based machine learning algorithm. Different cell types, particularly normal and cancerous cells, were effectively distinguished using their fluorescent fingerprints. Additionally, this DNA sensor array displayed excellent sensitivity to identify cellular alterations associated with chemical modulation of catabolic processes. Importantly, pharmacological effectors, which could modulate autophagic flux, have been effectively distinguished by generating responses from their global protein signatures. Taken together, these studies demonstrate that our multichannel DNA nanosensor is well suited for rapid identification of subtle changes in a complex mixture and thus can be readily expanded for point-of-care clinical diagnosis, high-throughput drug screening, or predicting the therapeutic outcome from a limited sample volume.

Sensing of citrulline modifications in histone peptides by deep cavitand hosts

Arrayed cavitand:fluorophore sensor complexes can selectively sense small citrulline modifications at arginine residues on post-translationally modified peptides. The sensor can differentiate between different numbers of citrulline modifications, and a simple two-fluorophore, 6-component array can effect cross-reactive discrimination between single modifications in aqueous solution.

Tailor-Made Engineering of Bioinspired Inks for Writing Barcode-like Multifunctional Sensory Electronics

This letter reports on a novel cost-efficient and multifunctional barcode-like sensors array (BLSA) printed with a conductive bioinspired smart ink. The conductive  ink (P@G ink), which can be further chemically engineered with different organic ligands, was generated via facile one-pot hydrothermal reduction of graphene oxide (GO) in dopamine (DA) as coreductan Usingvarious  chemical derivatives of the P@G inks on a flexible substrate (e.g., Kapton), a highly integrated BLSA as well as smart nose/tongue mimic array were generated for simultaneous sensing and distinguishing of complex physical and chemical stimuli, including temperature, light, air pressure, relative humidity, and volatile organic compounds (VOCs). Due to these very attractive features, the reported P@G ink-based BLSA would have the potential for unique opportunities regarding “all-in-one”—yet cost-effective—disposable electronics and sensors.