A colorimetric sensor array for odour visualization

PBI derivatives/surfactant-based fluorescent ensembles: Sensing of multiple aminoglycoside antibiotics and interaction mechanism studies

Fluorescent aggregates and ensembles have been widely applied in fabrication of fluorescent sensors due to their capacity of encapsulating fluorophores and modulating their photophysical properties. In the present work, fluorescent ensembles based on anionic surfactant SDS assemblies and perylene derivatives (PBIs) were particularly constructed. Three newly synthesized neutral PBI derivatives with different structures, PO, PC1 and PC2, were used for the purpose to evaluate probe structure influence on constructing fluorescent ensembles. The one with hydrophilic side chains, PO, experienced distinct photophysical modulation effect by SDS assemblies. The ensemble based on PO@SDS assemblies displayed effective fluorescence variation to antibiotic aminoglycosides (AGs). To improve cross-reactivity and discrimination capability of ensembles, a second probe, coumarin, was introduced into PO@SDS assemblies. The resultant ternary sensor, CM-PO@SDS, exhibited good qualitative and quantitative detection capabilities, and achieved differentiation of eight AGs and mixed AG samples both in aqueous solution and actual biological fluid, like human serum. Sensing mechanism studies revealed that hydrogen bonding, electrostatic and hydrophobic interactions are involved in the sensing process. This surfactant-based fluorescent ensemble provides a simple and feasible method for assessing AGs levels. Meanwhile, this work may provide some insights to design reasonable probes for constructing effective single-system based discriminative fluorescent amphiphilic sensors.

Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Esters-targeted colorimetric sensor array for the authenticity discrimination of strong-aroma baijiu with different origins

Baijiu authenticity has been a frequent problem driven by economic interests in recent years, so it is important to discriminate against baijiu with different origins. Herein, we proposed a simple and efficient esters-targeted colorimetric sensor array mediated by hydroxylamine hydrochloride. Esters undergo a nucleophilic addition reaction with hydroxylamine hydrochloride to form hydroxamic acid, which rapidly forms a purplish red ferric hydroxamate under FeCl3·6H2O. Bromophenol blue and rhodamine B enrich the color effects. The array detected 12 esters with a detection limit on the order of 10-5 of most esters and 16 mixed esters with R2 > 0.999 and recoveries close to 100%. Otherwise, for discriminating 34 strong-aroma baijius (SABs), the array has an accuracy of 98% according to the origin, and 95% according to the grades, with a response time of 1 min. This study provides a new strategy for authenticity determination and quality control of baijiu.

Multioxide combinatorial libraries: fusing synthetic approaches and additive technologies for highly orthogonal electronic noses

This study evaluates the performance advancement of electronic noses, on-chip engineered multisensor systems, exploiting a combinatorial approach. We analyze a spectrum of metal oxide semiconductor materials produced by individual methods of liquid-phase synthesis and a combination of chemical deposition and sol-gel methods with hydrothermal treatment. These methods are demonstrated to enable obtaining a fairly wide range of nanomaterials that differ significantly in chemical composition, crystal structure, and morphological features. While synthesis routes foster diversity in material properties, microplotter printing ensures targeted precision in making on-chip arrays for evaluation of a combinatorial selectivity concept in the task of organic vapor, like alcohol homologs, acetone, and benzene, classification. The synthesized nanomaterials demonstrate a high chemiresistive response, with a limit of detection beyond ppm level. A specific combination of materials is demonstrated to be relevant when the number of sensors is low; however, such importance diminishes with an increase in the number of sensors. We show that on-chip material combinations could favor selectivity to a specific analyte, disregarding the others. Hence, modern synthesis methods and printing protocols supported by combinatorial analysis might pave the way for fabricating on-chip orthogonal multisensor systems.

Porphyrin Atropisomerism as a Molecular Engineering Tool in Medicinal Chemistry, Molecular Recognition, Supramolecular Assembly, and Catalysis

Porphyrin atropisomerism, which arises from restricted σ-bond rotation between the macrocycle and a sufficiently bulky substituent, was identified in 1969 by Gottwald and Ullman in 5,10,15,20-tetrakis(o-hydroxyphenyl)porphyrins. Henceforth, an entirely new field has emerged utilizing this transformative tool. This review strives to explain the consequences of atropisomerism in porphyrins, the methods which have been developed for their separation and analysis and present the diverse array of applications. Porphyrins alone possess intriguing properties and a structure which can be easily decorated and molded for a specific function. Therefore, atropisomerism serves as a transformative tool, making it possible to obtain even a specific molecular shape. Atropisomerism has been thoroughly exploited in catalysis and molecular recognition yet presents both challenges and opportunities in medicinal chemistry.

Paper-based optical sensor arrays for simultaneous detection of multi-targets in aqueous media: A review

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media.

Smartphone-based detection and discrimination of amine vapors by a single dye-adsorbed material

Smartphone-assisted analysis has become widely utilized for detecting various species in recent years. In such studies, multiple dyes should be employed to ensure selectivity and analyte discrimination. In our research, we have demonstrated the capability of a specially synthesized dye to selectively detect and discriminate liquid amine vapors. The developed material employs meso-toluene-α,β,α',β'-tetrabromoBODIPY immobilized on a thin-layer chromatography plate, exhibiting structure-specific color changes in response to amine vapors. The hue values of these colors, observed under both ambient and UV light, enable discrimination even among closely related amine structures. A mobile application has also been developed for the rapid interpretation of test results.

Organic-Acid-Sensitive Visual Sensor Array Based on Fenton Reagent-Phenol/Aniline for the Rapid Species and Adulteration Assessment of Baijiu

Baijiu is an ancient, distilled spirit with a complicated brewing process, unique taste, and rich trace components. These trace components play a decisive role in the aroma, taste, and especially the quality of baijiu. In this paper, the redox reaction between the Fenton reagent and four reducing agents, including o-phenylenediamine (OPD), p-phenylenediamine (PPD), 4-aminophenol (PAP), and 2-aminophenol (OAP), was adopted to construct a four-channel visual sensor array for the rapid detection of nine kinds of common organic acids in baijiu and the identification of baijiu and its adulteration. By exploiting the color-changing fingerprint response brought by organic acids, each organic acid could be analyzed accurately when combined with an optimized variable-weighted least-squares support vector machine based on a particle swarm optimization (PSO-VWLS-SVM) model. What is more, this novel sensor also could achieve accurate semi-quantitative analysis of the mixed organic acid samples via partial least squares discriminant analysis (PLSDA). Most importantly, the sensor array could be further used for the identification of baijiu with different species through the PLSDA model and the adulteration assessment with the one-class partial least squares (OCPLS) model simultaneously.

Scalable Optical Nose Realized with a Chemiresistively Modulated Light-Emitter Array

Biological olfaction relies on a large number of receptors that function as sensors to detect gaseous molecules. It is challenging to realize artificial olfactory systems that contain similarly large numbers of sensory materials. It is shown that combinatorial materials processing with vapor deposition can be used to fabricate large arrays of distinct chemiresistive sensing materials. By combining these with light-emitting diodes, an array of chemiresistively-modulated light-emitting diodes, or ChemLEDs, that permit a simultaneous optical read-out in response to an analyte is obtained. The optical nose uses a common voltage source and ground for all sensing elements and thus eliminates the need for complex wiring of individual sensors. This optical nose contains one hundred ChemLEDs and generates unique light patterns in response to gases and their mixtures. Optical pattern recognition methods enable the quantitative prediction of the corresponding concentrations and compositions, thereby paving the way for massively parallel artificial olfactory systems. ChemLEDs open the possibility to explore demanding gas sensing applications, including in environmental, food quality monitoring, and potentially diagnostic settings.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.

Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

Characterization of the volatile flavor profiles of black garlic using nanomaterial-based colorimetric sensor array, HS-SPME-GC/MS coupled with chemometrics strategies

This work investigated the feasibility of applying headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC/MS) combining olfactory visualization for flavor characterization of black garlic. Volatile organic compounds (VOCs) analysis was performed to select important differential VOCs during black garlic processing. A multi-channels nanocomposite CSA assembled with two porous metal-organic frameworks was then developed to characterize flavor profiles changes during black garlic processing, and garlic samples during processing could be divided into five clusters, consistent with VOCs analysis. Artificial neural network (ANN) model outperformed other pattern recognition methods in discriminating processing stages. Furthermore, SVR model for odor sensory scores with the correlation coefficient for prediction set of 0.8919 exhibited a better performance than PLS model, indicating a preferable prediction ability for odor quality. This work demonstrated that the nanocomposite CSA combining appropriate chemometrics can offer an effective tool for objectively and rapidly characterizing flavor quality of black garlic or other food matrixes.

Enhanced Catalytic Activity of Binuclear Oxidovanadium(IV) Bisbenzimidazole Linked Porphyrin Dimer for the Generation of Biologically Active 3,4-Dihydropyrimidinones and Their Corresponding Thiones

Binuclear vanadyl(IV) porphyrin (V2BP), where two vanadium(IV) porphyrin macrocycles are linked through benzimidazole units at the β-positions, has been prepared and characterized with various techniques, such as UV-vis, Fourier transform-infrared, electron paramagnetic resonance, cyclic voltammetry, density functional transform calculations, and mass spectrometry. V2BP exhibits a red shift (Δλmax = 10 nm) in the Soret band as compared with unsubstituted parent vanadyl(IV) meso-tetraphenylporphyrin (VP). The synthesized binuclear vanadyl(IV) porphyrin (V2BP) has further been studied as a catalyst to explore a single-pot multicomponent Biginelli reaction producing biologically active 3,4-dihydropyrimidin-2-(1H)-one (DHPM)-based biomolecules and the corresponding thiones under solvent-free conditions and its catalytic activity has been compared with vanadyl(IV) meso-tetraphenylporphyrin (VP). Several reaction conditions, such as the amount of catalyst, time, solvent, and temperature, have been optimized to obtain the maximum yield of DHPMs or thiones. The synthesized β-functionalized V2BP porphyrin dimer manifests much higher conversion (84-95% yield) of DHPMs or the corresponding thiones under the optimized reaction conditions with high TON (4454-5037) and TOF (1113-1259 h-1) values for the one-pot multicomponent Biginelli reaction as compared to the literature. The catalyst exhibited excellent recyclability up to 10 cycles.

Molecular Symmetry and Art: Visualizing the Near-Symmetry of Molecules in Piet Mondrian's De Stijl

Symmetry and shape are essential aspects of molecular structure and how we interpret molecules and their properties. We, as chemists, are comfortable with pictorial representations of structure, in which some nuance is lost-investigating molecular shape numerically by looking at how closely it fits a reference, such as a plane, or a set of vectors or coordinates, is informative, though far from engaging. Often relationships between chemical structure and derived values are obscured. Taking our inspiration from Piet Mondrian's Compositions, we have depicted the symmetry information encoded within 3D data as blocks of color, to show clearly how chemical arguments and resultant molecular distortion may contribute to symmetry. Great art gives us a new perspective on the world; as a pastiche, this art may allow us to look at familiar molecules, such as porphyrins, in a new light, understanding how their shape and properties are intertwined.

Unveiling the potential of polymer cholesteric liquid crystal interpenetrating networks as a label-free alcohol biochemical sensor

In this study, an optical sensor is developed, incorporating hydrogen-bonded photonic array dots containing poly(acrylic acid) (PAA) within a polymer cholesteric liquid crystal interpenetrating polymer network (PCLCIPN) framework, thereby effectively controlling porosity. This methodology involves the fabrication of a porous photonic film, subsequent infusion with a hydrogel (PAA), and precise UV-curing to generate patterned array dots. The sensor exhibits exceptional discriminatory capability between methanol and ethanol, accurately discerning their varying concentrations within alcohol solutions. The optical sensing performance of the film is rigorously evaluated through continuous monitoring of wavelength shifts in the transmission spectrum across various alcohol concentrations. Notably, the observed wavelength shifts demonstrate a linear correlation with the concentration of alcohol, thereby enabling precise quantitative analysis of the alcohol solutions. The sensor exhibits a sensitivity of 0.44 nm/% for ethanol concentrations ranging from 5% to 60%, increasing to 2.1 nm/% for concentrations between 60% and 80%. Similarly, for methanol, sensitivities of 0.68 nm/% (5-60%) and 2.2 nm/% (60-80%) are recorded. Remarkably, this sensitivity trend extends seamlessly to 1 : 1 ethanol/methanol ratios, with values of 0.49 nm/% (5-60%) and 2.25 nm/% (60-80%). Furthermore, these sensors demonstrate colorimetric response to different alcohols, rendering them accessible and cost-effective biosensors for visual detection, thus obviating the necessity for complex analytical instruments.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

Coherent Modulation of the Aggregation Behavior and Intramolecular Charge Transfer in Small Molecule Probes for Sensitive and Long-term Nerve Agent Monitoring

Aggregation-induced emission (AIE) shows promising performance in chemical sensing relying on the change of the emission behavior of the probe molecule monomers to the aggregated product. However, whether the response contrast could be further boosted by utilizing the emission property of the aggregated probe and the aggregated product remains a big challenge. Here, an exciting AIE probe regulation strategy was proposed by coherently modulating the aggregation behavior and the intramolecular charge transfer (ICT) property of the probes and thus an aggregated-to-aggregated colorimetric-fluorescent dual-mode detection was achieved. The blue emissive film obtained with the optimal AIE probe has been proven to be effective to recognize the vapor of nerve agent analog DCP in air by emitting a sharp green fluorescence. In addition, a porous polymer-based wet sensing chip loaded with the probe enables the immediate response to DCP vapor with a limit of detection (LOD) of 1.7 ppb, and it was further integrated into a wearable watch device for long-term monitoring of DCP vapor up to two weeks. We expect the present probe design strategy would greatly deepen the AIE-based science and provide new insights for long-term monitoring sensors toward trace hazardous substances.

"Covalent-Disassembly"-Based Approaches For Sensing Applications

The detection of analytes with small molecular probes is crucial for the analysis and understanding of chemical, medicinal, environmental and biological situations as well as processes. Classic detection approaches rely on the concept of molecular recognition and bond formation reactions. Bond breakage reactions have been less explored in similar contexts. This concept article introduces metal-salen and metal-imine complexes as "covalent-disassembly"-based (DB)-probes for detecting polyoxophosphates, thiols, amino acids, HCN and changes in pH. It discusses the roles, importance and combinations of structurally functionalized molecular building blocks in the construction of DB-probes. Applications of optimized DB-probes for analyte detection in live cells and foodstuff are also discussed. Furthermore, the mechanism of the disassembly of a Fe(III)-salen probe upon pyrophosphate binding is presented. Extraordinary selectivity for this analyte was achieved by a multistep disassembly sequence including an unprecedented structural change of the metal complex (i. e. "induced-fit" principle). Design principles of probes for sensing applications following the "covalent-disassembly" approach are summarized, which will help improving current systems, but will also facilitate the development of new DB-probes for challenging analytic targets.

Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease

Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.

π-Extended Porphyrin-Phthalocyanine Heterojunction Devices Exhibiting High Ammonia Sensitivity with a Remarkable Light Effect

π-Extended porphyrins represent an attractive class of organic compounds because of their unique photophysical, optoelectronic, and physicochemical properties. Herein, cross-conjugated (Ace-PQ-Ni) and linear-conjugated (AM6) porphyrins are used to build double-layer heterojunction devices by combining them with a lutetium bisphthalocyanine complex (LuPc2). The heterojunction effect at the porphyrin-phthalocyanine interface plays a key role in the charge transport properties. Both devices exhibit exceptionally high ammonia sensitivity at room temperature and under ambient relative humidity, with limit of detection values of 156 and 115 ppb for Ace-PQ-Ni/LuPc2 and AM6/LuPc2 sensors, respectively. Interestingly, the Ace-PQ-Ni/LuPc2 and AM6/LuPc2 sensors display opposite effects upon light illumination. While the former sensors show largely decreased ammonia sensitivity under light illumination, the current variation of the latter under ammonia is remarkably enhanced with a multiplication factor of 13 and a limit of detection (LOD) of 83 ppb. The striking difference in their sensing properties upon light illumination is attributed to their different π-conjugation pathways (cross-conjugation versus linear conjugation).

Actuating Liquid Crystals Rapidly and Reversibly by Using Chemical Catalysis

Microtubules and catalytic motor proteins underlie the microscale actuation of living materials, and they have been used in reconstituted systems to harness chemical energy to drive new states of organization of soft matter (e.g., liquid crystals (LCs)). Such materials, however, are fragile and challenging to translate to technological contexts. Rapid (sub-second) and reversible changes in the orientations of LCs at room temperature using reactions between gaseous hydrogen and oxygen that are catalyzed by Pd/Au surfaces are reported. Surface chemical analysis and computational chemistry studies confirm that dissociative adsorption of H2 on the Pd/Au films reduces preadsorbed O and generates 1 ML of adsorbed H, driving nitrile-containing LCs from a perpendicular to a planar orientation. Subsequent exposure to O2 leads to oxidation of the adsorbed H, reformation of adsorbed O on the Pd/Au surface, and a return of the LC to its initial orientation. The roles of surface composition and reaction kinetics in determining the LC dynamics are described along with a proof-of-concept demonstration of microactuation of beads. These results provide fresh ideas for utilizing chemical energy and catalysis to reversibly actuate functional LCs on the microscale.

Comprehensive Qualitative and Quantitative Colorimetric Sensing of Volatile Organic Compounds Using Monolayered Metal-Organic Framework Films

Cross-responsive chemical sensors are in high demand owing to their ability to distinguish a broad range of analytes. In this study, a vapochromic sensor array based on metal-organic frameworks (MOFs), which exhibits distinct patterns when exposed to volatile organic compounds (VOCs) and humidity, is developed. Conventional sensor arrays consist of various receptors that produce different responses. The vapochromic MOF-based sensor comprises dicopper paddlewheel clusters and dimethylamine azobenzene as binary colorimetric sensing moieties. Upon exposure to VOCs, the constructed sensor encompasses a broad spectrum of colors, ranging from green to red. Furthermore, the color of the MOF is influenced by the solvent used during the pretreatment. Consequently, monolayered MOF thin films can be adapted to multicomponent array systems by immersing the MOF in different solvents. This system provides both qualitative and quantitative sensing, generating unique color patterns corresponding to specific VOC types. Notably, the sensor successfully discriminates each of 14 common VOCs and water and accurately categorizes unknown samples. Moreover, the system undergoes reversible color changes in response to humidity, obviating the need for high-temperature regeneration steps. This novel approach offers insights into the versatile applications of MOFs by creating a colorimetric sensor array capable of detecting various analytes.

Triple-Emission Single Sensing Element-Enabled Ratiometric Fluorescent Array Identification of Multiple Antibiotics

Given that intricate toxicological profiles exist among different antibiotics and pose serious threats to the environment and human health, synchronous analysis of multiple residues becomes crucial. Sensor arrays show potential to achieve the above purpose, but it is challenging to develop easy-to-use and high-sensitivity tools because the state-of-the-art arrays often require more than one recognition unit and are monosignal dependent. Here we exquisitely designed a fluorescent nanoprobe (2-aminoterephthalic acid-anchored CdTe quantum dots with Eu3+ coordination, CdTe-ATPA-Eu3+) featuring triple emissions at the same excitation as the only element to fabricate a luminescent sensor array with ratiometric calculations for identifying multiple antibiotics. By taking tetracycline, chlortetracycline, doxycycline, oxytetracycline, penicillin G, and sulfamethoxazole as models, the six species exhibited distinguishable motivation or/and quenching impacts on the three emissions of CdTe-ATPA-Eu3+, which were employed as indicators to perform the ratiometric logical operation and further combined with pattern recognition analysis for multitarget determination. Evidently, such a design exhibits two advances: (1) with the triple-emission probe as the sole receptor requiring neither internal nor external adjustments, the fabricated array acts as an extremely facile tool for multianalyte detection; (2) the ratiometric calculations offer excellent sensitivity and reliability for high-performance determination. Consequently, accurate identification and quantification of individual antibiotics and their combinations at various levels were verified in both laboratory and practical matrices. Our work provides a new tool for simultaneously detecting multiple antibiotics, and it will inspire the development of advanced sensor arrays for multitarget analysis.

Unlocking Diabetic Acetone Vapor Detection by A Portable Metal-Organic Framework-Based Turn-On Optical Sensor Device

Despite exhaled human breath having enabled noninvasive diabetes diagnosis, selective acetone vapor detection by fluorescence approach in the diabetic range (1.8-3.5 ppm) remains a long-standing challenge. A set of water-resistant luminescent metal-organic framework (MOF)-based composites have been reported for detecting acetone vapor in the diabetic range with a limit of detection of 200 ppb. The luminescent materials possess the ability to selectively detect acetone vapor from a mixture comprising nitrogen, oxygen, carbon dioxide, water vapor, and alcohol vapor, which are prevalent in exhaled breath. It is noteworthy that this is the first luminescent MOF material capable of selectively detecting acetone vapor in the diabetic range via a turn-on mechanism. The material can be reused within a matter of minutes under ambient conditions. Industrially pertinent electrospun luminescent fibers are likewise fabricated alongside various luminescent films for selective detection of ultratrace quantities of acetone vapor present in the air. Ab initio theoretical calculations combined with in situ synchrotron-based dosing studies uncovered the material's remarkable hypersensitivity toward acetone vapor. Finally, a freshly designed prototype fluorescence-based portable optical sensor is utilized as a proof-of-concept for the rapid detection of acetone vapor within the diabetic range.

Determination of procymidone residues in rapeseed oil based on olfactory visualization technology

A new means about olfactory visualization technique for the quantitative analysis of procymidone residues in rapeseed oil has been proposed. First, an olfactory visualization system was set up to collect volatile odor information from rapeseed oil samples containing different concentrations of procymidone residues. Then, we utilized four intelligent optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA), to optimize the characteristics of the sensors. Finally, support vector machine regression (SVR) models employing optimized features were constructed for the quantitative detection of procymidone residues in rapeseed oil. The study demonstrated that the SA-SVR model demonstrated superior prediction results, achieving a high determination coefficient of prediction (R P 2 ) at 0.9894. As indicated by the results, it is possible to successfully conduct non-destructive detection of procymidone residues in edible oil by the olfactory visualization technology.

A local point of view of the Cu(100) → NiTPP charge transfer at the NiTPP/Cu(100) interface

A precise understanding, at the molecular level, of the massive substrate → adsorbate charge transfer at the NiTPP/Cu(100) interface has been gained through the application of elementary symmetry arguments to the structural determination of the NiTPP adsorption site by photoelectron diffraction (PED) measurements and Amsterdam density functional calculations of the free D4h NiTPP electronic structure. In particular, the PED analysis precisely determines that, among the diverse NiTPP chemisorption sites herein considered (fourfold hollow, atop, and bridge), the fourfold hollow one is the most favorable, with the Ni atom located at 1.93 Å from the surface and at an internuclear distance of 2.66 Å from the nearest-neighbors of the substrate. The use of elementary symmetry considerations enabled us to provide a convincing modeling of the NiTPP-Cu(100) anchoring configuration and an atomistic view of the previously revealed interfacial charge transfer through the unambiguous identification of the adsorbate π* and σ* low-lying virtual orbitals, of the substrate surface atoms, and of the linear combinations of the Cu 4s atomic orbitals involved in the substrate → adsorbate charge transfer. In addition, the same considerations revealed that the experimentally reported Ni(II) → Ni(I) reduction at the interface corresponds to the fingerprint of the chemisorption site of the NiTPP on Cu(100).

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

Ammonia and Humidity Sensing by Phthalocyanine-Corrole Complex Heterostructure Devices

The versatility of metal complexes of corroles has raised interest in the use of these molecules as elements of chemical sensors. The tuning of the macrocycle properties via synthetic modification of the different components of the corrole ring, such as functional groups, the molecular skeleton, and coordinated metal, allows for the creation of a vast library of corrole-based sensors. However, the scarce conductivity of most of the aggregates of corroles limits the development of simple conductometric sensors and requires the use of optical or mass transducers that are rather more cumbersome and less prone to be integrated into microelectronics systems. To compensate for the scarce conductivity, corroles are often used to functionalize the surface of conductive materials such as graphene oxide, carbon nanotubes, or conductive polymers. Alternatively, they can be incorporated into heterojunction devices where they are interfaced with a conductive material such as a phthalocyanine. Herewith, we introduce two heterostructure sensors combining lutetium bisphthalocyanine (LuPc2) with either 5,10,15-tris(pentafluorophenyl) corrolato Cu (1) or 5,10,15-tris(4-methoxyphenyl)corrolato Cu (2). The optical spectra show that after deposition, corroles maintain their original structure. The conductivity of the devices reveals an energy barrier for interfacial charge transport for 1/LuPc2, which is a heterojunction device. On the contrary, only ohmic contacts are observed in the 2/LuPc2 device. These different electrical properties, which result from the different electron-withdrawing or -donating substituents on corrole rings, are also manifested by the opposite response with respect to ammonia (NH3), with 1/LuPc2 behaving as an n-type conductor and 2/LuPC2 behaving as a p-type conductor. Both devices are capable of detecting NH3 down to 10 ppm at room temperature. Furthermore, the sensors show high sensitivity with respect to relative humidity (RH) but with a reversible and fast response in the range of 30-60% RH.

Visual Sensor Arrays for Distinction of Phenolic Acids Based on Two Single-Atom Nanozymes

Although great achievements have been made in the study of artificial enzymes, the design of nanozymes with high catalytic activities of natural enzymes and the further establishment of sensitive biosensors still remain challenging. Here, two nanozymes, i.e., ZnCoFe three-atom nanozyme (TAzyme) and Sn single-atom nanozyme (SAzyme)/Ti₃C₂T_x, are developed, which show peroxidase-like catalytic activities by catalyzing the reaction of hydrogen peroxide (H₂O₂), 4-aminoantipyrine (4-AAP), and phenolic acids to generate colorimetric reactions. The involvement of different phenolic acids leads to the generation of different color products. These subtle color-variation profiles between these phenolic acids prompt us to exploit an electronic tongue based on the two nanozymes to distinguish phenolic acids. Data interpretation by the pattern recognition method, such as linear discriminant analysis (LDA), displays good clustering separation of six different phenolic acids at concentrations of 0.1 μM to 1 mM, validating the effectiveness of the colorimetric nanozyme sensor array.

Simultaneous quantitation of free fatty acid in rice by synergetic data fusion of colorimetric sensor arrays, NIR, and MIR spectroscopy

This study evaluated the feasibility of colorimetric sensor array (CSA), near-infrared (NIR) and mid-infrared (MIR) spectroscopy for quantitation of free fatty acids in rice using data fusion. Purposely, different data sets of low-level (CSA-NIR_LL, CSA-MIR_LL, and NIR-MIR_LL) and mid-level (CSA-NIR_ML, CSA-MIR_ML, and NIR-MIR_ML) fusion were adopted to enhance the statistical parameters. The model performance was evaluated using coefficient of determination for prediction, (R²p), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD). Synergetic low-level and mid-level fusion model yielded 0.7707 ≤ R²p ≤ 0.8275, 14.4 ≤ RMSEP ≤ 16.3 and 2.19 ≤ RPD ≤ 2.48; and 0.7788 ≤ R²p ≤ 0.8571, 12.4 ≤ RMSEP ≤ 16.8 and 2.12 ≤ RPD ≤ 2.88, respectively. The CSA-NIR_ML model delivered an optimal performance for prediction of free fatty acid. The integration of CSA, NIR and MIR was feasible and could improve the prediction accuracy of free fatty acids in rice.

A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma

Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS-least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs.

Metalloporphyrin reduced C70 fullerenes as adsorbents and detectors of ethenone; A DFT, NBO, and TD-DFT study

In the present work, the structure and electronic properties of Ti-, Cr-, Fe-, Ni-, Zn-, and Cu-inserted in porphyrin-reduced C70 fullerenes (TM-PIC70Fs) and their interactions with the ethenone were studied using DFT, NBO, and TD-DFT at CAM-B3LYP/6-31G(d) level of theory. 2.89-3.83 and 4.02-4.56 eV were obtained for the HOMO-LUMO gap energies and work functions of TM-PIC70Fs, respectively, compared with 3.76 and 4.54 eV for PIC70F. Among considered TM-PIC70Fs, the adsorption of the ethenone on Ti-PIC70F appreciably changed the HOMO-LUMO energy gap and work function. Consequently, Ti-PIC70F may be used as the ethenone's electronic conductivity and work function types sensor. According to calculated UV-visible spectra, the ethenone adsorption may change the color of Fe- and Ti-PIC70Fs. Therefore, they can be used as color-changing sensors of ethenone. In addition, Ti-, Cr-, Fe-, and Zn-PIC70Fs can be employed as suitable adsorbents of ethenone. Among proper sensors and adsorbents of ethenone, Cr-, Fe-, and Zn-PIC70Fs may be recovered and reused.

Threshold-Responsive Colorimetric Sensing System for the Continuous Monitoring of Gases

Colorimetric sensors are widely used because of their inherent advantages including accuracy, rapid response, ease-of-use, and low costs; however, they usually lack reusability, which precludes the continuous use of a single sensor. We have developed a threshold-responsive colorimetric system that enables repeated analyte measurements by a single colorimetric sensor. The threshold responsive algorithm automatically adjusts the sensor exposure time to the analyte and measurement frequency according to the sensor response. The system registers the colorimetric sensor signal change rate, prevents the colorimetric sensor from reaching saturation, and allows the sensor to fully regenerate before the next measurement is started. The system also addresses issues common to colorimetric sensors, including the response time and range of detection. We demonstrate the benefits and feasibility of this novel system, using colorimetric sensors for ammonia and carbon dioxide gases for continuous monitoring of up to (at least) 60 detection cycles without signs of analytical performance degradation of the sensors.

Harnessing the cation-π interactions of metalated gold monolayer-protected clusters to detect aromatic volatile organic compounds

The strong, non-covalent interactions between π-systems and cations have been the focus of numerous studies on biomolecule structure and catalysis. These interactions, however, have yet to be explored as a sensing mechanism for detecting trace levels of volatile organic compounds (VOCs). In this article, we provide evidence that cation-π interactions can be used to elicit sensitive and selective chemiresistor responses to aromatic VOCs. The chemiresistors are fitted with carboxylate-linked alkali metals bound to the surface of gold monolayer-protected clusters formulated on microfabricated interdigitated electrodes. Sensor responses to aromatic and non-aromatic VOCs are consistent with a model for cation-π interactions arising from association of electron-rich aromatic π-systems to metal ions with the relative strength of attraction following the order K⁺ > Na⁺ > Li⁺. The results point toward cation-π interactions as a promising research avenue to explore for developing aromatic VOC-selective sensors.

Paper-Based Multiplex Sensors for the Optical Detection of Plant Stress

The rising population and the ongoing climate crisis call for improved means to monitor and optimise agriculture. A promising approach to tackle current challenges in food production is the early diagnosis of plant diseases through non-invasive methods, such as the detection of volatiles. However, current devices for detection of multiple volatiles are based on electronic noses, which are expensive, require complex circuit assembly, may involve metal oxides with heating elements, and cannot easily be adapted for some applications that require miniaturisation or limit front-end use of electronic components. To address these challenges, a low-cost optoelectronic nose using chemo-responsive colorimetric dyes drop-casted onto filter paper has been developed in the current work. The final sensors could be used for the quantitative detection of up to six plant volatiles through changes in colour intensities with a sub-ppm level limit of detection, one of the lowest limits of detection reported so far using colorimetric gas sensors. Sensor colouration could be analysed using a low-cost spectrometer and the results could be processed using a microcontroller. The measured volatiles could be used for the early detection of plant abiotic stress as early as two days after exposure to two different stresses: high salinity and starvation. This approach allowed a lowering of costs to GBP 1 per diagnostic sensing paper. Furthermore, the small size of the paper sensors allows for their use in confined settings, such as Petri dishes. This detection of abiotic stress could be easily achieved by exposing the devices to living plants for 1 h. This technology has the potential to be used for monitoring of plant development in field applications, early recognition of stress, implementation of preventative measures, and mitigation of harvest losses.

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.

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.

Fullerene Complexation in a Hydrogen-Bonded Porphyrin Receptor via Induced-Fit: Cooperative Action of Tautomerization and C-H···π Interactions

A supramolecular chiral hydrogen-bonded tetrameric aggregate possessing a large cavity and tetraarylporphyrin substituents was assembled using alternating 4H- and 2H-bonds between ureidopyrimidinone and isocytosine units, respectively. The aggregation mode was rationally shifted from social to narcissistic self-sorting by changing urea substituent size only. The H-bonded tetramer forms a strong complex with C₆₀ guest, at the same time undergoing remarkable structural changes. Namely, the cavity adjusts to the guest via keto-to-enol tautomerization of the ureidopyrimidinone unit and as a result, porphyrin substituents move apart from each other in a scissor blade-like opening fashion. The rearrangement is accompanied by C-H···π interaction between the alkyl solubilizing groups and the nearby placed porphyrin π-systems. The latter interaction was found to be crucial for the guest complexation event, providing energetic compensation for otherwise costly tautomerization. We showed that only the systems possessing sufficiently long alkyl chains capable of interacting with a porphyrin ring are able to form a complex with C₆₀. The structural rearrangement of the tetramer was quantitatively characterized by electron paramagnetic resonance pulsed dipolar spectroscopy measurements using photogenerated triplets of porphyrin and C₆₀ as spin probes. Further exploring the C-H···π interaction as a decisive element for the C₆₀ recognition, we investigated the guest-induced self-sorting phenomenon using scrambled tetramer assemblies composed of two types of monomers possessing alkyl chains of different lengths. The presence of the fullerene guest has enabled the selective scavenging of monomers capable of C-H···π interaction to form homo-tetrameric aggregates.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.

Challenges and Opportunities for Printed Electrical Gas Sensors

Printed electrical gas sensors are a low-cost, lightweight, low-power, and potentially disposable alternative to gas sensors manufactured using conventional methods such as photolithography, etching, and chemical vapor deposition. The growing interest in Internet-of-Things, smart homes, wearable devices, and point-of-need sensors has been the main driver fueling the development of new classes of printed electrical gas sensors. In this Perspective, we provide an insight into the current research related to printed electrical gas sensors including materials, methods of fabrication, and applications in monitoring food quality, air quality, diagnosis of diseases, and detection of hazardous gases. We further describe the challenges and future opportunities for this emerging technology.

Smartphone-Assisted Sensing of Trinitrotoluene by Optical Array

Here we report the design and fabrication of an array-based sensor, containing functionalized Carbon Dots, Bodipy's and Naphthalimide probes, that shows high fluorescence emissions and sensitivity in the presence of low amounts of TNT explosive. In particular, we have fabricated the first sensor device based on an optical array for the detection of TNT in real samples by using a smartphone as detector. The possibility to use a common smartphone as detector leads to a prototype that can be also used in a real-life field application. The key benefit lies in the possibility of even a nonspecialist operator in the field to simply collect and send data (photos) to the trained artificial intelligence server for rapid diagnosis but also directly to the bomb disposal unit for expert evaluation. This new array sensor contains seven different fluorescent probes that are able to interact via noncovalent interactions with TNT. The interaction of each probe with TNT has been tested in solution by fluorescence titrations. The solid device has been tested in terms of selectivity and linearity toward TNT concentration. Tests performed with other explosives and other nitrogen-based analytes demonstrate the high selectivity for TNT molecules, thus supporting the reliability of this sensor. In addition, TNT can be detected in the range of 98 ng∼985 μg, with a clear different response of each probe to the different amounts of TNT.

Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B1 in Peanuts by Olfactory Visualization Technique

This study proposes a novel method for detection of aflatoxin B₁ (AFB₁) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB₁ in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (R_P) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 μg·kg^-1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB₁ in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.

Environmental monitoring of trace metal pollutants using cellulosic-paper incorporating color change of azo-chromophore

An essential requirement for colorimetric paper-sensor is to allow the target analytes (heavy metal ions) to access the chromophore while maintaining strong chromophore immobilization on the porous substrate surface. This work evaluates the selection of sensitive chromophores (dithizone, 1-(2-pyridylazo) 2-naphthol and 4-(2-pyridylazo)-resorcinol) and their immobilization strategies on paper sensors. Dithizone (DTz) are capable of producing a significant color transition at unadjusted pH, observed by UV-Vis absorption spectroscopy and visible recognition. After immobilizing DTz on a paper substrate (cellulose acetate/chitosan substrate), the DTz-paper sensor showed a distinctive color change from blue-green to peach-pink upon reaction with Pb²⁺ ions, and the color intensity was proportional to the metal concentration. Quantitative analysis using RGB (R:Red; G:Green; B:Blue) plots showed that increasing DTz concentration on the CA/CS paper sensor increases the difference in total color intensity (∆I_T) and the difference in red code intensity (∆I_R). This is due to the formation of more DTz-Pb²⁺ complexes on the CA/CS paper substrate. The CA/CS paper strips immobilized with 100 ppm DTz showed practical potential for rapid detection of heavy metal ions. The DTz-CA/CS paper sensor showed significant color change when detecting spiked heavy metals ions (0.1 ppm Pb²⁺, 2.0 ppm Zn²⁺, and 0.2 ppm Cu²⁺) in river water samples that prepared at the maximum permissible limit for industrial effluent in Malaysia.

Volatile Organic Compound Sensing Array and Optoelectronic Filter System using Ion-Pairing Dyes with a Wide Visible Spectrum

An ideal dye-based sensing array has essential design requirements, including facile preparation methodology, tolerance to water vapor, a broad range of color-responsive changes, and a simple readout system. Here, a brief synthetic route is developed for ion-pairing dyes exhibiting unusual chromatic changes across the entire visible spectrum. It requires only mixing and precipitation under mild conditions. The dyes are applied to a sensing array containing 12 sensing elements with different initial states. Owing to the numerous color variations of the dyes, the color map generated by the array is highly simple yet sufficiently accurate to distinguish among the different functional groups (such as amines, aldehydes, and carboxylic acids) as well as carbon chain lengths. Principle component analysis (PCA) demonstrates that volatile organic compounds (VOCs) can be well classified according to the color changes of the sensing array. The ion-pairing dyes are embedded into 3D stacked nanofibers via electrospinning, and function as effective harmful-gas (e.g., formaldehyde) sensors with sub-ppm theoretical detection limits (0.15 ppm). Finally, the 3D stacked nanofibers can be employed in an optoelectronic filter system that automatically checks for formaldehyde in the surroundings and also confirms the effective removal of the detected formaldehyde by the gas filter cartridge.