In-field Detection Research Articles

Functionalised Al(III) metal organic frameworks for fluorescence sensing of nitroaromatic vapours

Mixed-ligand Al(iii) metal–organic frameworks of the MIL-53 structural type show strong fluorescence quenching upon interaction with vapour-phase nitroaromatic compounds.

BCNO quantum dots-based ratiometric fluorescence platform integrated with portable device: Hypoxanthine sensing for on-site assessment of meat freshness with deep learning

Deep learning algorithms have been applied to digital image colorimetry (DIC) to improve color measurement and analysis, enabling the identification and quantification of analytes. In this work, we developed a smartphone-assisted platform for the detection of hypoxanthine (Hx) based on a ratiometric fluorescent probe (RF-probe) and deep learning algorithm for the first time with advantages of high sensitivity, in-field and simple operation, and accurate detection. Through an enzymatic cascade reaction, xanthine oxidase (XOD) degraded Hx to uric acid (UA) and H2O2, o-phenylenediamine (OPD) was converted to 2,3-diaminophenoxazine (DAP) by catalytic oxidation of H2O2 and OPD under horseradish peroxidase (HRP). The presence of DAP led to fluorescence quenching of BCNO quantum dots (BCNO QDs) at 440 nm and fluorescence enhancement of DAP at 570 nm due to the internal filtration effect (IFE). With the addition of Hx, the fluorescence color changed from blue to yellow. A good linear relationship was between I570/I440 and the Hx concentration in the range of 3-100 µM with a detection limit of 50 nM. Fluorescent color images of solution and test strips were acquired and extracted with a smartphone of our designed miniature device, which could be used to establish DIC for the detection of Hx with the linear relationship ranging from 0-100 μM and 0-40 μM, respectively. Then YOLOv5-assisted deep learning was applied to recognize and deal with the images, which offered improved accuracy, faster inference speed, flexibility, and efficient training. Therefore, our simple DIC platform was established for the in-field detection of Hx in real meat samples which was significant for the analysis of meat freshness and provided broad application potential for the analysis of other targets in food safety.

A microfluidic concentration gradient colorimetric system for rapid detection of nitrite in surface water

A microfluidic concentration gradient colorimetric detection system consisting of a microfluidic concentration gradient colorimetric detection chip, a self-built colorimetric signal acquisition box and a self-written smartphone APP was constructed for the rapid, in-field and visual quantitative detection of nitrite. Specifically, nitrite with initial concentration of C0 can be automatically diluted into 8 concentration gradients characterized by arithmetic series, and the concentrations are 0, 0.20 C0, 0.33 C0, 0.46 C0, 0.59 C0, 0.72 C0, 0.86 C0 and C0. The colorimetric signal acquisition box avoided the interference of light spots on data acquisition. Under the optimal experimental conditions, the quantitative detection of nitrite was achieved by the proposed two-step colorimetric method based on the inhibition of AuNPs signal amplification, and the limit of detection (LOD) was 0.14 mg/L. The microfluidic concentration gradient colorimetric detection system was able to detect nitrite as low as 0.43 mg/L and showed a good specificity. The practical application was investigated by analyzing 10 actual samples of river and lake water, pure water and tap water. The recoveries of the microfluidic concentration gradient colorimetric detection system ranged from 94.92% to 105.60%, which indicates that the method had a good application prospect in the detection of practical samples.

Recombinase Polymerase Amplification Assay for Rapid Field Diagnosis of Stewart’s Wilt of Corn Pathogen Pantoea stewartii subsp. stewartii

Stewart’s vascular wilt and leaf blight of sweet corn is caused by the Gram-negative enteric bacterium Pantoea stewartii subsp. stewartii. Stewart’s wilt results in substantial yield losses worldwide warranting rapid and accurate disease diagnosis. Recombinase polymerase amplification (RPA) is an isothermal technique that is tolerant to host plant-derived inhibitors and is, therefore, ideally suited for rapid in-field detection vis-à-vis traditional polymerase chain reaction-based molecular assays. An RPA assay coupled with a Lateral Flow Device (LFD) was developed for rapid, accurate, and sensitive real-time detection of P. stewartii subsp. stewartii directly from the infected host offering in-field pathogen detection, timely disease management, and satisfying quarantine and phytosanitary requirements. Twelve novel primer sets were designed against conserved genomic regions of P. stewartii subsp. Stewartii; however, only the primers for amplification of the intergenic spacer region between capsular polysaccharide genes cpsA and cpsB were discernibly unique and adequate for unambiguous identification of P. stewartii subsp. stewartii. The P. stewartii subsp. stewartii-specific primers were further validated in a simplex RPA assay for specificity against twenty-six bacterial species representing several Pantoea and other closely related bacterial species/subspecies/strains found in the same niche, and naturally or artificially infected plant samples. The integrated RPA/LFD assay was also optimized for rapid and sensitive on-site detection of P. stewartii subsp. stewartii with an empirical detection limit of 0.0005 pg μL−1 bacterial DNA and 1 × 102 CFU mL−1 (app. two bacterial cells used per RPA reaction) in minimally processed samples for accurate, low-cost, and point-of-need diagnosis of the quarantine pathogen P. stewartii subsp. stewartii.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network

BackgroundDetection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.ResultsIn our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).ConclusionsExtensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

Syringe-driven biosensing of Salmonella typhimurium using nuclear track membrane filtration and nanozyme signal amplification

A syringe-driven colorimetric biosensor was constructed for simple detection of Salmonella typhimurium (S. ty) based on nuclear track membrane filtration and nanozyme signal amplification. First, immune [email protected] nanozymes (IGPNs) and large-volume sample in centrifugal tube were blow-mixed by push-and-pull of syringe. After S. ty-IGPN conjugates and free IGPNs was pulled into syringe, the syringe was connected with a uniform-size nuclear track membrane (NTM) and the mixture was pushed to flow through, resulting in selective trap of larger-size conjugates and effective remove of smaller-size IGPNs. Then, conjugates on the NTM were washed and H2O2-TMB was pulled to immerse the NTM. The catalysate was finally photographed and measured through smartphone App for detemination of S. ty. This biosensor enabled simple and in-field bacterial detection as low as 12 CFU/mL in 15 min. Compared with common sandwich-structured biosensors using paired antibodies, this biosensor with easier extension, lower cost and shorter time only required one antibody to functionalize nanozymes.

An instrument-free photoluminescent sensor array based on “turn-off” bimetallic lanthanide metal–organic frameworks for antibiotic determination

The widespread use of antibiotics results in harmful residues, posing a serious threat to ecosystems and human health. Given synergetic or antagonistic toxicological features often exist among different antibiotics, it is vital to develop methods to synchronously determine these contaminants. Herein, we develop a hand-held photoluminescent sensor array based on “turn-off” bimetallic lanthanide metal–organic frameworks (MOFs) to realize the identification and quantification of six common antibiotics. Three Tb/Eu-MOFs with different bimetallic proportions were synthesized and employed to provide signals toward multiple antibiotics based on their differential contributions to the photoluminescence quenching of these probes. By designing a 3D-printed smartphone-equipped signal capturing platform, tiny luminescence variations induced by different antibiotics were recorded and processed for the classification and identification of these targets via pattern recognition analysis. Compared to conventional sensor arrays requiring cumbersome signal reading instruments and complex sensing units, our platform really enables instrument-free in-field detection of multiple targets with only a basic material. The platform could identify not only single antibiotics in a wide level scope but also various combinations of two, three or even four antibiotics with good discrimination capacity. Differentiation of multiple antibiotics in river water, milk, and urine matrices was also demonstrated. Furthermore, over 90% accuracy was gained for unknown samples in double-blind tests. Our work offers a portable, low-cost, and high-performance tool to determine multiple antibiotics simultaneously, which will stimulate future exploration of hand-held sensor arrays with simple sensing elements for on-site analysis and point-of-care diagnosis.

Remote detection of fungal pathogens in viticulture using laser-induced fluorescence: an experimental study on infected potted vines

IntroductionPathogenic fungi, such as Plasmopara viticola and Erysiphe necator, severely threaten the annual yield of grapes in both quantity and quality. In contrast to other crop production systems, fungicides are intensively applied in viticulture as a countermeasure. The goal of precision viticulture is to optimize vineyard performance as well as the environmental impact by reducing fungicides and applying different techniques and combined strategies. Therefore, new emerging technologies are required, including non-invasive detection, as well as monitoring and tools for the early and in-field detection of fungal development. MethodsIn this study, we investigated leaves of potted vines (Vitis vinifera cv. ‘Riesling’) and traced the development of the inoculated leaves using our new remote detection system vinoLAS®, which is based on laser-induced fluorescence spectroscopy. We ran a measurement campaign over a period of 17 days.ResultsWe were able to detect a leaf infection with P. viticola, the causal agent of downy mildew, between 5 and 7 days after inoculation. Our results provide evidence for a successful application of laser-based standoff detection in vineyard management in the future. Thus, the vinoLAS system can serve as a model technology for the detection of pathogenic disease symptoms and thus monitoring complete vineyard sites. This allows for early countermeasures with suitable crop protection approaches and selected hot-spot treatments.DiscussionAs P. viticola is considered one of the most damaging fungi in European viticulture, disease mapping via this monitoring tool will help to reduce fungicide applications, and will, therefore, support the implementation of the European Green Deal claims.

Responsive luminescent silver-based metal-organic frameworks for highly sensitive and selective detection of hydrogen sulfide in biological system via a self-assembled headspace separation device

As a highly toxic gas pollutant and also an endogenous gaseous signaling molecule existing in a variety of physiological processes, the rapid and accurate in-field detection of hydrogen sulfide is of great concern. Nevertheless, two drawbacks as for the optical probes for H2S detection, taking about a long time to reach the optical signal balance or the low selectivity, always exist. Herein, by using a highly photoluminescent and H2S-stimuli responsive silver-based metal-organic frameworks (MOFs): Ag-BDC (BDC = 1, 4-benzene dicarboxylate), we demonstrated that the luminescence intensity of Ag-BDC MOFs was inversely proportional to the concentration of H2S due to the Ag–S coordination and the obstruction of ligand-to-metal charge transfer (LMCT) transition process, and there was a quick response time of below 3.0 min. Combined with a simple customized device to separate H2S from the sample, the selectivity of the method for H2S detection could be greatly improved, and no interference would be caused even if the other sulfur-containing species coexisted. The luminescence probe presented a favorable sensitivity within a linear range of 0.1–1000 μM along with a detection limit of 23.7 nM. When employed to assay the endogenous sulfide level in the human serum and mouse brain tissue, the approach showed recoveries from 96.3% to 102% with relative standard derivation (RSD) less than 2.0%. By the integration of the responsive luminescent silver-based MOFs with a simple self-assembled headspace separation device, obviously the present strategy could be beneficial to the development and design of the in-field fast H2S measurement, possessing particular advantages in biological systems to eliminate the potential interferences.

Engineering of an adaptive tandem CRISPR/Cas12a molecular amplifier permits robust analysis of Vibrio parahaemolyticus

Seeking new molecular diagnostic method for pathogenic bacteria detection is of utmost importance for ensuring food safety and protecting human health. Herein, we have engineered an adaptive tandem CRISPR/Cas12a molecular amplifier specifically designed for robust analysis of vibrio parahaemolyticus (V. parahaemolyticus), one of the most harmful pathogens. Our strategy involves the integration of three crucial processes: recombinase polymerase amplification (RPA) for copy number amplification, terminal deoxynucleotidyl transferase (TdT) for template-free strand elongation, and CRISPR/Cas12a-mediated trans-cleavage of a reporter molecule. By combining these processes, the target genomic DNA extracted from V. parahaemolyticus is able to activate many CRISPR/Cas12a units (CRISPR/Cas12an) simultaneously, resulting in a greatly amplified target signal to indicate the presence and concentration of V. parahaemolyticus. This unique model offers more advantages compared to traditional amplification models that use one RPA amplicon to activate one CRISPR/Cas12a unit. Under optimized conditions, our method enables the detection of target V. parahaemolyticus within a linear range of 1 × 102–1 × 107 CFU/mL, with an impressive limit of detection as low as 12.4 CFU/mL. It is conceivable that the adaptive tandem CRISPR/Cas12a molecular amplifier could be adapted as routine diagnostic kits in future for in-field detection of pathogens.

STLs for GPUs: Using High-Level Language Approaches

Self-Test Libraries (STLs) are widely used for in-field fault detection in processor-based systems. Currently, their adoption is being extended to Graphics Processing Units (GPUs), due to their increasing usage in the safety-critical domain, and the demand for effective in-field functional safety mechanisms mandated by the functional safety standards (e.g., ISO 26262 for automotive). This work describes a method to develop suitable STLs resorting to High-Level Languages (HLLs) (e.g., CUDA) for some modules in GPUs, thus reducing the complexity of the STLs development process. We also discuss the method’s main advantages/limitations, as well as the challenges and constraints when developing HLL STLs for GPUs. Furthermore, we outline those cases demanding the usage of Low-Level Languages (LLLs) for the implementation of STLs. The evaluation and validation of the method resort to the FlexGripPlus GPU model, implementing one micro-architecture of NVIDIA. The experimental results show that HLL STLs can be effectively developed for regular modules in the GPU.

Oriented-aptamer encoded magnetic nanosensor with laser-induced fluorescence for ultrasensitive test of okadaic acid

Okadaic acid (OA) biotoxin acts a well-established inhibitor of protein phosphatase even a tumor promoter of human being, arouse great attention in safety monitoring. However, the powerful and convenient nanosensing technologies for addressing the demands such as rapidity, high sensitivity, and stability in the in-field test of OA shellfish toxin is still scarce. Herein, a high-performance magnetic biometric nanosensor (MBNS) integrating oriented aptamers and ultrasensitive laser-induced fluorescence (LIF) was firstly proposed for the in-field detection of trace OA in seafoods. High-density aptamers hybridized with FAM-labeled cDNA were tethered to the surface of AuNPs on magnetic MIL-101@Fe3O4, and then finely regulated by mercaptohexyl alcohol (MCH) to be orderly assembled, as was successfully utilized to engineer an active biological nanosensor for highly specific recognition of OA. Aptamers anchored on magnetic Fe3O4@MOF@AuNPs activate a biometric microreactor of OA, in which the superior LIF properties, conformation regulation of aptamer, and the specific recognition using aptamer genes were adopted. The magnetic nanosensor with an excellent specificity and super sensitivity for OA analysis was achieved within 20 min. Moreover, the content of captured OA could facilely be recorded by measuring the fluorescence intensity, and the limit of detection (LOD) and limit of quantitation of OA (LOQ) reached 0.015 and 0.050 ng/mL respectively, which was far better than most aptamer-based biometric sensing methods. The feasibility for accurate test of trace OA toxin in the fortified shellfish samples was validated with the recovery yields of 88.2–107.5% and RSD of 0.5–7.6%, respectively. The result demonstrated that the oriented-aptamer encoded MNS had significant practical values in rapid and ultrasensitive detection of OA biotoxin and the related safety applications.

Advanced Ultrasonic Inspection of Thick-Section Composite Structures for In-Field Asset Maintenance.

An investigation into the inspection capabilities of in-field advanced ultrasound detection for use on ultra-thick (20 to 100 mm) glass fibre-reinforced polyester composites is presented. Plates were manufactured using custom moulding techniques, such that delamination flaws were created at calibrated depths. The full matrix capture technique with an on-board total focussing method was used to detect flaws scanned by a 0.5 MHz linear array probe. Flaw through-thickness dimensions were altered to assess the threshold for crack face separation at which delaminations could be identified. Furthermore, part thickness and in-plane flaw dimensions were varied to identify the inspection capability limitations of advanced ultrasonics for thick composites. The results presented in this study demonstrate an inverse relationship between the ability to find delaminations and plate thicknesses, with inspections successful at depths up to 74 mm. When the delamination thickness exhibited surface-to-surface contact, the inspection capability was reduced to 35 mm. There was an exponential decay relationship between the accuracy of the flaw depth measurement and plate thickness, likely due to the necessity of low probe frequencies. The effective inspection depth was determined to be in the range of 1 to 20 times the wavelength. It is speculated that the accuracy of measurements could be improved using probes with novel coupling solutions, and detectors with optimised signal processing/filtration algorithms.

In-Field Tobacco Leaf Maturity Detection with an Enhanced MobileNetV1: Incorporating a Feature Pyramid Network and Attention Mechanism

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model’s robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves.

Portable Mass Spectrometry Approach Combined with Machine Learning for Onsite Field Detection of Huanglongbing Disease.

Huanglongbing (HLB) is one of the most serious citrus diseases in the world. Rapid, onsite, and accurate field detection of HLB is a challenging task in analytical science for a long time. Herein, we have developed a novel HLB detection method that combines headspace solid phase microextraction with portable gas chromatography-mass spectrometry (PGC-MS) approach for onsite field detection of volatile metabolites of citrus leaves. Detectability and characteristics of HLB-affected metabolites from leaves were validated, and the important biomarkers were verified by authentic compounds. A machine learning approach based on random forest algorithm is established to model the volatile metabolites from healthy, symptomatic, and asymptomatic citrus leaves. In this work, a total of 147 citrus leaf samples were analyzed. Analytical performances of this newly developed method were investigated by in-field detection of various volatile metabolites. Results demonstrated limits of detection and quantification of 0.04-0.12 and 0.17-0.44 ng/mL for different metabolites, respectively. Linear calibration curves of various metabolites were established over a concentration dynamic range of at least three orders (R2 > 0.96). Good reproducibility was obtained for intraday (3.0-17.5%, n = 6) and interday precision (8.7-18.2%, n = 7). This new HLB field detection method provides a rapid detection with 6 min for each sample via a simple optimized procedure, including onsite sampling, PGC-MS analysis, and data process and provides a high accuracy (93.3%) for simultaneous identification of healthy, symptomatic, and asymptomatic trees. These data support the use of this new method for reliable field detection of HLB. Furthermore, metabolic pathways of HLB-affected metabolites were also proposed. Overall, our results not only provide a rapid and onsite field HLB detection method but also provide valuable information for understanding metabolic change of HLB infection.

Fast In-Field Determination of Trace Fe Ion in Steam-Water for Safety Management of Thermal Electric Power Plants

A fast in-field tool was proposed to detect ppb level Fe ion in the steam-water to monitor the corrosion of the pipeline in time in electric power plants. The detection method was based on fluorescent quenching of graphene oxide quantum dots (GOQDs) at the existence of Fe ion. A piece of glass wool was used as the test strip and embedded in a microfluidic chip, while the easily removable microfluidic chip ensures a continuous rapid in-field detection and the architecture of glass wool acted as a three-dimensional porous support for well dispersive loading of GOQDs. Application specific integrated circuit was developed to enhance the sensitivity, robustness and power-efficiency of this detector and reduce its size and cost. In result, the potable fluorescence detector exhibited approximately a linear detection range from approximately 0.5 ppb to 30 ppm, a rapid response in 4 minutes and an acceptable error compared to the standard laboratory method.

Biopsy Assessment of Oncologic Control 3 Years Following Primary Partial Gland Cryoablation: A Prospective Cohort Study of Men With Intermediate-risk Prostate Cancer.

We evaluated 3-year oncologic outcomes following primary partial gland cryoablation. Men with unilateral intermediate-risk prostate cancer undergoing primary partial gland cryoablation since March 2017 enrolled in a prospective outcome registry. The postablation protocol for all men included surveillance prostate biopsy at 2 years postablation and reflex prostate biopsy for cases with high suspicion of recurrence (eg, progressive rise in PSA). Recurrence of clinically significant prostate cancer was defined as any Gleason grade group ≥2 disease on postablation biopsy. Freedom from failure represented no whole gland salvage treatment, metastatic prostate cancer, or prostate cancer mortality. Freedom from recurrence and freedom from failure were characterized using nonparametric maximum likelihood estimators. A total of 132 men had at least 24 months of follow-up data. Biopsies identified clinically significant prostate cancer in 12 men. At 36 months, model-estimated rates of freedom from recurrence of in-field, out-of-field, and overall clinically significant cancer were 97% (95% CI: 92-100), 87% (95% CI: 80-94), and 86% (95% CI: 78-93), respectively. The model-estimated proportion with freedom from failure at 36 months was 97% (95% CI: 93-100). The low in-field cancer detection rate at 3 years indicates successful ablation of localized cancers. Conversely, our observed out-of-field detection rate highlights the need for continued surveillance following partial gland cryoablation. Many of these recurrences exhibited very low volume of clinically significant disease below the detection threshold of multiparametric MRI, suggesting a limited role for multiparametric MRI in detecting clinically significant recurrences at 2 years. These findings emphasize the need for long-term surveillance and identification of predictors of clinically significant prostate cancer recurrences to guide biopsy timing.

Highly Sensitive Ethylene Sensors Based on Ultrafine Pd Nanoparticles-Decorated Porous ZnO Nanosheets and Their Application in Fruit Ripeness Detection

Ethylene is the most common ripening phytohormone in fruits, and excess ethylene can overripen the fruit. However, the in-field detection of ethylene is still limited. In this work, ultrafine Pd nanoparticles-decorated porous ZnO nanosheets (UPNP ZnO nanosheets) were conveniently synthesized through a facile solvent reduction method. The UPNP ZnO nanosheets were characterized using scanning electron microscopy, transmission electron microscopy, energy dispersive spectrum, X-ray diffraction and X-ray photoelectron spectroscopy. The ZnO nanosheets were uniformly coated with Pd nanoparticles. The size of the Pd nanoparticle was very small, with a diameter of approximately 2 nm. Due to the unique structure of the porous ZnO nanosheets and the excellent catalytic properties of the ultrafine Pd nanoparticles, the as-prepared samples showed very high sensing performance in ethylene detection. The lowest detection concentration was 10 ppb, which is the lowest detection limit to our knowledge. It has been proved that the decoration of ultrafine Pd nanoparticles can largely increase the relative percentage of chemisorbed oxygen and deficient oxygen, which are benefits for ethylene oxidation, and actually accelerate the process of the sensing reaction. Furthermore, the UPNP ZnO nanosheets can even be applied in fruit maturity detection. Using mangos as an example, our experiment revealed that the response of UPNP ZnO nanosheets to mangos at different maturity stages was quite different. This result suggests that our product has broad application prospects in monitoring fruit ripening stage.

Highly Selective and Quantitative Point-of-Care Diagnostic Method for Adrenaline.

Adrenaline, also known as epinephrine, is a neurotransmitter/hormone that is an important target in diagnostics. Development of an effective method for detecting it in the presence of other neurotransmitters is a challenging task. The electrochemical and fluorescent techniques commonly used have low selectivity in distinguishing among catecholamines. Herein, a small-molecule organic probe with an activated furfural moiety is reported to exploit the nucleophilicity of epinephrine to generate a bright-colored donor-acceptor Stenhouse adduct. Among nine common neurotransmitters or their analogues, only epinephrine was found to generate a unique colour change discernible with the naked eye, whereas the other ones remain unaffected. Under various in-field detection conditions, including solution, droplet, and paper strip-based detection, the colour change were also noticeable. The low detection limit of 1.37 nM and a limit of quantitation of 4.37 nM were achieved with simple UV/Vis methods in addition to the sub-ppm level sensing under visual conditions with naked eyes. The probe could be used for practical colorimetric measurements as a point-of-care tool without any complex and expensive machinery, making this approach accessible to all. In addition, using a simple smartphone, the determination of epinephrine concentrations is possible by using machine-learning techniques.

Sensitive Evanescence-Field Waveguide Interferometer for Aqueous Nitro-Explosive Sensing

The development of novel chemical nitro-explosive sensors with high sensitivity, low cost and a compact size is essential for homeland security, environmental protection and addressing military challenges. Polymeric optical waveguides based on refractive index sensing are widely used in biochemical detection due to their advantages of large-scale integration, low cost, high sensitivity and anti-electromagnetic interference. In this study, we designed and fabricated a polymer waveguide Mach–Zehnder interferometer (MZI) sensor to detect 2,4-dinitrotoluene (DNT) in water. One phase shifter of the MZI waveguide was functionalized by coating a thin cladding layer of polycarbonate with dipolar chromophores and used as the sensing arm; the other arm was coated with passive epoxy resin cladding and used as the reference arm. The phase difference between the two arms of the MZI was modulated using the refractive index (RI) change in the polycarbonate cladding when dipolar chromophores interacted with electro-deficient DNT. The theoretical sensitivity of the designed MZI can reach up to 24,696 nm/RIU. When used for explosive detection, our fabricated sensor had a maximum wavelength shift of 4.465 nm and good linear relation, with an R2 of 0.96 between the wavelength shift and a concentration ranging from 3.5 × 10−5 to 6.3 × 10−4 mol/L. The sensitivity of our device was 6821.6 nm/(mol/L). The design of an unbalanced MZI sensor, together with the sensing material, provides a new approach to using low-cost, compact and highly sensitive devices for in-field explosive detection.