Hand-held Sensor Research Articles

Monitoring soil carbon in smallholder carbon projects: insights from Kenya

Voluntary carbon market schemes facilitate funding for projects promoting sustainable land management practices to sequester carbon in natural sinks such as biomass and soil, while also supporting agricultural production. The effectiveness of VCM schemes relies on accurate measurement mechanisms that can directly attribute carbon accumulation to project activities. However, measuring carbon sequestration in soils has proven to be difficult and costly, especially in fragmented smallholdings predominant in global agriculture. The cost and accuracy limitations of current methods to monitor soil organic carbon (SOC) limit the participation of smallholder farmers in global carbon markets, where they could potentially be compensated for adopting sustainable farming practices that provide ecosystem benefits. This study evaluates nine different approaches for SOC accounting in smallholder agricultural projects. The approaches involve the use of proximal and remote sensing, along with process models. Our evaluation centres on stakeholder requirements for the Measurement, Reporting, and Verification system, using the criteria of accuracy, level of standardisation, costs, adoptability, and the advancement of community benefits. By analysing these criteria, we highlight opportunities and challenges associated with each approach, presenting suggestions to enhance their applicability for smallholder SOC accounting. The contextual foundation of the research is a case study on the Western Kenya Soil Carbon Project. Remote sensing shows promise in reducing costs for direct and modelling-based carbon measurement. While it is already being used in certain carbon market applications, transparency is vital for broader integration. This demands collaborative work and investment in infrastructure like spectral libraries and user-friendly tools. Balancing community benefits against the detached nature of remote techniques is essential. Enhancing information access aids farmers, boosting income through improved soil and crop productivity, even with remote monitoring. Handheld sensors can involve smallholders, given consistent protocols. Engaging the community in monitoring can cut project costs, enhance agricultural capabilities, and generate extra income.

An artificial intelligence handheld sensor for direct reading of nickel ion and ethylenediaminetetraacetic acid in food samples using ratiometric fluorescence cellulose paper microfluidic chip

User-friendly in-field sensing protocol is crucial for the effective tracing of intended analytes under less-developed countries or resources-limited environments. Nevertheless, existing sensing strategies require professional technicians and expensive laboratory-based instrumentations, which are not capable for point-of-care on-site analyses. To address this issue, artificial intelligence handheld sensor has been designed for direct reading of Ni2+ and EDTA in food samples. The sensing platform incorporates smartphone with machine learning-driven application, 3D-printed handheld device, and cellulose paper microfluidic chip stained with ratiometric red-green-emission carbon dots (CDs). Intriguingly, Ni2+ introduction makes green fluorescent (FL) of CDs glow but red FL fade because of the coordination of Ni2+ with CDs verified by density functional theory (DFT), concurrently manifesting continuous FL colour transition from red to green. Subsequent addition of EDTA renders FL of CDs-Ni2+ recover owing to the capture of Ni2+ from CDs by EDTA based on strong chelation effect of EDTA on Ni2+ confirmed via DFT, accompanying with a noticeable colour returning from green to red. Inspired by above FL phenomena, CDs-based cellulose paper microfluidic chips are first fabricated to facilitate point-of-care testing of Ni2+ and EDTA. Designed fully-automatic handheld sensor is utilized to directly output Ni2+ and EDTA concentration in water, milk, spinach, bread, and shampoo based on wide linear ranges of 0–48 μM and 0–96 μM, and low limits of detection of 0.274 μM and 0.624 μM, respectively. The proposed protocol allows for speedy straightforward on-site determination of target analytes, which will trigger the development of automated and intelligent sensors in near future.

A near-linear transect sampling method for validation of SMAP surface soil moisture in non-complex terrains

ABSTRACT The global validation for SMAP (Soil Moisture Active Passive) Level-4 surface soil moisture using well-established core validation sites does not comprehensively account for all landscapes on earth’s surface due to the diverse nature of their intrinsic characteristics. Due to the inhibitive cost of implementing a standard validation site, this study presents an alternative sampling approach suitable for localized validation of SMAP data in non-complex terrains. It involves clustering a large heterogeneous study area to smaller units of non-complex terrains where landscape-defining characteristics are largely homogeneous, thus permitting the computation of areal soil moisture as a simple arithmetic mean of near-linear point measurements. This allows optimization of limited resources (a hand-held moisture sensor with site-specific calibrations) to balance the need for spatial representativeness of samples in the sampling unit and the need for temporal proximity of sampled measurements to the aggregation time of the satellite product being validated. For ease of movement, transect sampling is implemented along access roads that run across the sampling units to allow sufficient measurement replications within a reasonably short time. Validations with four different landscapes in Kenya show a good agreement between in situ measurements and SMAP with R2 of 0.76, 0.72, 0.80, and 0.82, and biases of −0.0246, +0.0113, 0.0004, and + 0.0035 m3 m−3, respectively for Mawego, Kuresoi, Sotik and Kapsuser sites. These results only marginally differ from those obtained with a spatially distributed sampling method, indicating the potential of the proposed sampling design for time and cost effectiveness in validations at non-complex terrains. An analysis of the temporal variability of SMAP soil moisture in the watershed is also presented, with an assessment of its significance in the selection of sampling sites for validation. Particularly, the concept of temporal stability of soil moisture as a basis for clustering validation sites is evaluated.

The response of a sorghum sudangrass cover crop to residual nitrogen and its relationship with spectral sensors

AbstractA sorghum sudangrass (SSG) cover crop grown after a cash crop could take up residual nitrogen (N) before it is lost. As in‐field monitoring of SSG properties is laborious, predicting biomass and N concentrations with spectral sensors could be useful. At two sites in Live Oak, Florida, we evaluated the response of SSG to residual N from previous N fertilization and the performance of handheld and satellite sensors in estimating SSG properties. We quantified aboveground biomass, plant N, leaf greenness (NDVI), net potential N mineralization (PNM), and soil permanganate oxidizable carbon (POXC). Residual N did not affect SSG properties, PNM was highest at the highest N input rate in one site, and soil POXC was correlated with SSG properties (biomass and plant N). NDVI measured from a handheld sensor better predicted SSG properties than satellite imagery in these small plots, suggesting a greater potential to be a useful management tool.

Facile barometric calibration of photoluminescence-based oxygen sensors on a standard rotary evaporator system with a digital vacuum pump

A simple setup and methodology for barometric calibration of quenched phosphorescence oxygen sensors are described, which utilize a standard rotary evaporator (Rotavap) system equipped with a digital vacuum pump. They were demonstrated with a panel of different sensing materials based on PtBP dye, including solid-state sensor coatings on different substrates (membrane inserts, stickers or inner wall of the vacuum flask), as well as liquid sensor formulations. The sensors inside the flask were exposed to different conditions created by the Rotavap system (dry or humid air, water, temperatures ranging 2°C-40°C, residual total pressure), and their intensity, lifetime or phase shift signals were measured with handheld sensor readers Optech (Mocon) and Firesting (PyroScience) in a contact-less manner to generate multi-point calibration curves. Several critical points of the method, which require careful consideration, have been revealed, including sensor mounting inside the Rotavap, its temperature control during calibration, choice of the reader. Finally, a detailed characterization of several sensor types was carried out and their key operational parameters (τ0, KSV, heterogeneity, T-coefficients), and calibration equations for the different conditions were determined and compared to each other and the results of conventional O2 calibration. Overall, barometric calibration method using Rotavap provides a useful alternative to conventional O2 calibration methods, being simple, accurate, convenient and versatile.

Robust Autonomous Mobile Manipulation for Substation Inspection

Abstract The need for autonomous infrastructure inspections performed by mobile robots is becoming increasingly prevalent, to mitigate human error and inspect critical infrastructure with increased frequency, while reducing costs. Electric distribution substations contain a variety of high-power equipment that may occasionally fail, and we focus on inspecting pothead compartments as a representative test case. Frequent measurements of acoustic and transient earth voltage data can indicate degradation before failures occur. Handheld partial discharge (PD) sensors can gather these types of data. Measurements using PD sensors can be automated using a mobile manipulation platform with a custom end-effector. Accurate mapping, localization, and navigation are required to perform autonomous inspection tasks in indoor environments with unmanned ground vehicles. Computer vision and precise manipulator control are necessary for successful handheld sensor interactions. In this paper, we present and analyze a custom-integrated mobile manipulation system capable of performing these functions, which achieves a tradeoff between the small size needed to navigate through low-clearance areas, and the reach capabilities needed to collect the required measurements from pothead compartments.

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy.

A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device's hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.

Assessing the accuracy of an infrared-converted drone camera with Orange-Cyan-NIR filter for vegetation and environmental monitoring

Drones equipped with cameras sensitive to near-infrared wavelengths are increasingly being used in environmental assessment studies and in agriculture. These cameras are sensitive to vegetation cover, extent of eutrophication in water bodies, and aspects of crops such as growth vigour, biomass and potential yield. Single-sensor (‘RGB’) cameras with modified spectral filters that allow for capturing near-infrared wavelengths offer a low-cost alternative to multi-sensor multispectral cameras or spectrometers. However, some studies point to lower measurement accuracies by such infrared converted sensors. So, to what extent can infrared converted cameras be used to quantify vegetation condition? This case study compared vegetation indices calculated from infrared converted camera imagery to those measured by a drone-borne multispectral camera and a handheld NDVI meter, as captured over soybean and potato fields. The study found that infrared converted camera derived NDVI was consistently lower over vegetation than NDVI measured from the multispectral and handheld sensors. Further, all considered indices of the infrared converted camera displayed relative underestimation at high index values, but over estimation at low index values. The study builds on previous case studies with similar results by further evaluating the reflectance patterns of the individual image bands to find possible reasons for the discrepancy in vegetation index measurements. There is good agreement between the near-infrared bands of the respective sensors (r=0.87), but the respective red bands have weak correlation (r=−0.03). We discuss possible reasons for the lower vegetation index measurements observed by the infrared converted camera, noting broad band sensitivities, and differing central wavelengths, which may have caused overestimated reflectance in the red band. All processing and analysis were executed with open-source software, and source code is made available to support reproducible research.

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

Surface-enhanced Raman spectroscopy (SERS) substrate based on gold nanostars–silver nanostars for imidacloprid detection

Surface-enhanced Raman spectroscopy (SERS) is a powerful molecular spectroscopy technique that combines Raman spectroscopy with nanostructured metallic surfaces to amplify the Raman signals of target molecules by more than 103. The high sensitivity of SERS poses a significant opportunity for pesticide detection in complex matrices at ultralow concentrations. In this study, we improved the SERS sensitivity for imidacloprid (IMD) by employing silver nanostars (AgNs) coated with gold nanostars (AuNs) as the SERS-active substrate. The SERS response towards IMD detection increased based on the combination of AuNs and AgNs on the substrate surface. The intensity of the SERS signal of IMD using the AuNs/AgNs substrate increased compared to using individual metal nanoparticle substrates. The excellent reproducibility of SERS intensity using the AuNs/AgNs substrate was achieved with a low relative standard derivative (RSD) of 4.87% for 20 different spots on the same sample and 5.19% for 20 different samples. This detection system can be used for multiple tests, which is crucial for the advancement of handheld sensors designed for field use, where minimal or no high-level technical support is accessible.

Eu3+-based InP/ZnS quantum dot fluorescence platform for multi-color and sensitive visualization of tetracycline

A turn-on type ratiometric fluorescence sensing system of blue quantum dot Eu-MPA-InP/ZnS was established for multi-color visualization determination of tetracycline (TC). Mercaptopropionic acid (MPA)-capped InP/ZnS quantum dots (MPA-InP/ZnS QDs) both modify the hydrophilicity of InP/ZnS QDs and serve as a scaffold for coordinating of Eu3+ ions. The blue fluorescence of Eu-MPA-InP/ZnS at 478 nm is reduced by the TC through the inner filter effect (IFE) under a single excitation wavelength of 365 nm. Rich colour gradients and a highly discriminative colour change were features of this multicolour response to TC, which allowed visual quantification of TC in a dose-dependent manner. Furthermore, by cross-linking Eu-MPA-InP/ZnS with agarose (Aga.), a mouldable Eu-MPA-InP/ZnS@Aga 96-well gel sensing device was designed to serve as a handheld sensor for on-site detection of TC. This probe expands the use of InP QDs in analytical sensing and has been effectively applied to the visual detection of tetracycline in milk and the environment.

Quantification of oxygen consumption in head and neck cancer using fluorescent sensor foil technology.

Head and neck squamous cell carcinoma (HNSCC) patients suffer from frequent local recurrences that negatively impact on prognosis. Hence, distinguishing tumor and normal tissue is of clinical importance as it may improve the detection of residual tumor tissue in surgical resection margins and during imaging-based surgery planning. Differences in O2 consumption (OC) can be used to this aim, as they provide options for improved surgical, image-guided approaches. In the present study, the potential of a fluorescent sensor foil-based technology to quantify OC in HNSCC was evaluated in an in vitro 3D model and in situ in patients. In vitro measurements of OC using hypopharyngeal and esophageal cell lines allowed a specific detection of tumor cell spheroids embedded together with cancer-associated fibroblasts in type I collagen extracellular matrix down to a diameter of 440 µm. Pre-surgery in situ measurements were conducted with a handheld recording device and sensor foils with an oxygen permeable membrane and immobilized O2-reactive fluorescent dyes. Lateral tongue carcinoma and carcinoma of the floor of the mouth were chosen for analysis owing to their facilitated accessibility. OC was evaluated over a time span of 60 seconds and was significantly higher in tumor tissue compared to healthy mucosa in the vicinity of the tumor. Hence, OC quantification using fluorescent sensor foil-based technology is a relevant parameter for the differentiation of tumor tissue of the head and neck region and may support surgery planning.

Nitrogen retrieval in grapevine (Vitis vinifera L.) leaves by hyperspectral sensing

Nitrogen (N) is a critical macronutrient that directly affects grapevine yield and quality but is also highly mobile in soil and can cause environmental contamination when over-applied. Monitoring leaf N content is useful for assessing vine N status and thereby improving N management plans, and the ability to assess N by remote sensing is desirable. Remote sensing technology has been utilized for retrieving N in agronomic crops since the 1990s. However, remote sensing has not been widely adopted for N management mainly due to variability in spectral patterns that emerge when training a model on one dataset and then applying it to a different dataset. Differences in environmental conditions, phenological stages, and plant varieties contribute to variability. This study aimed to understand the major factors limiting the generalizability of N-level retrieval algorithms in grapevine: the impact of leaf age and how calibrating nitrogen content, using either leaf dry mass (Nmass) or leaf area (Narea), influence the outcomes. This study also compares the performance among five major N retrieval approaches. We used spectral data from a hand-held hyperspectral sensor measuring wavelengths from 350 to 2500 nm from 664 individual leaf samples obtained from two grapevine varieties on three different sampling campaigns. Our findings indicate that while machine learning and chemometric approaches offer high accuracy levels (R2 = 0.78), the physical modeling approach utilizing the PROSPECT radiative transfer model (RTM) is capable of consistently retrieving N independent of specific dataset conditions and with an acceptable level of accuracy (R2 = 0.45) while using only 50% of the hyperspectral data. The estimation of Nmass using Proteinmass, which was calculated from Proteinarea retrieved by RTM and ground truth LMA, demonstrates a high potential for consistent retrieval of Total N (TN) in grapes. We also found that while calibrating a model by Narea can eliminate the negative impact of leaf age on prediction accuracy, Nmass still performed better in four out of five approaches tested. This study shows that a hybrid model that combines machine learning and RTM can alleviate the impact of leaf age on prediction accuracy while reducing 50% of spectral bands required and maintaining an acceptable level of accuracy (R2 = 0.54).

UAS and UGV-Based Disease Management System for Diagnosing Corn Diseases Above and Below the Canopy Using Deep Learning

Highlights Generated custom imagery dataset using UAS, UGV, and handheld sensors separately in diseased corn fields. Proposed data pipeline utilizing Google Sheets API to establish communication between each platform and enable access through a web application. Developed deep learning-based disease management system for above and below-the-canopy corn disease diagnosis. Trained and evaluated disease detection models from each platform separately to provide management recommendations. Abstract. Early disease management following the onset of disease symptoms is crucial for controlling their spread. Heterogenous collaboration between unmanned aerial systems (UAS) and unmanned ground vehicles (UGV) for field scouting and disease diagnosis is a potential approach for developing automated disease management solutions. However, automation of crop-specific disease identification requires the use of above and below-canopy sensors and properly trained deep learning (DL) models. This research proposes to develop a novel disease management system for diagnosing corn diseases from above and below the canopy by collaboratively using edge devices mounted on UAS and UGV, respectively. Three separate datasets were acquired using UAS above the canopy, UGV below the canopy, and handheld imaging platforms within diseased corn fields. DL-based image classification models were first trained for identifying common corn diseases under field conditions, resulting in up to 95.04% testing accuracy using the DenseNet169 architecture. After creating bounding box annotations for disease images, You Only Look Once (YOLO)v7 DL-based object detection models were trained to identify diseases from each platform separately. Trained YOLOv7 models resulted in the highest mAP@IoU=0.5 of 37.6%, 46.4%, and 72.2% for locating and identifying diseases above the canopy using UAS, below the canopy using UGV, and handheld sensors, respectively. A client/server architecture was developed to establish communication between the UAS, UGV, and Google Spreadsheets via Wi-Fi communication protocol. The coordinates of diseased regions and distinct disease types were recorded using the client/server architecture on Google Spreadsheets. A web application utilized the data from the Google Spreadsheet to help users diagnose diseases in real time and provide recommendations for implementing appropriate disease management practices. This study reports findings of independently operated UAS and UGV that can potentially offer disease spread from below and over the corn canopy by combining the information. Keywords: Deep learning, Disease identification, Disease management, Object detection, UAS, UGV, YOLOv7.

Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy.

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

Neighborhood-Level Air Pollution Monitoring and Analysis in Massachusetts

Air pollution is a significant environmental issue with far-reaching consequences for both human health and the planet. It arises from the emission of harmful pollutants, such as particulate matter, ozone, and nitrogen dioxide, into the atmosphere. These pollutants originate from various sources, including vehicles, industrial facilities, and power plants. The impacts of air pollution are wide-ranging, encompassing respiratory infections, cardiovascular disease, cancer, harm to ecosystems, and contributions to climate change. The study of air pollution is a multidisciplinary field that draws upon knowledge from physics, chemistry, biology, and engineering. Understanding the causes and effects of air pollution is crucial for developing effective strategies to mitigate its harmful effects. Physics, in particular, plays a vital role in this field by providing the tools and techniques required to measure and comprehend the behavior of pollutants. By examining air pollution, we gain insights into the factors driving this problem and can devise measures to reduce pollution levels. Moreover, this knowledge empowers individuals and societies to make informed decisions about minimizing exposure to pollutants and formulating policies and regulations that safeguard both human well-being and the environment. In our study conducted in the central Massachusetts region, we investigated the status of air pollution using a combination of Environmental Protection Agency (EPA) monitoring sites and hand-held sensors. While the EPA sites offer longterm monitoring data, hand-held devices’ flexible and affordable nature allowed us to explore air quality at the local neighborhood and street levels. By utilizing these tools, we assessed the spatial and temporal variations in air pollution within the city, aiding in the identification of localized hotspots. Such information is valuable for targeting specific areas requiring interventions and further understanding the dynamics of pollution distribution.

UiO-66-NH2-PEI combined with a hand-held catalytic combustion sensor for trace acetone detection in exhaled breath

The trace acetone determination in breath can be regarded as a noninvasive method for diagnostic of diabetes. There is an urgent demand to develop a simple method for acetone detection which can be easily performed at home. Here, UiO-66-NH2@PEI with the appropriate aperture size combined with the hand-held sensor was used to construct the adsorption–desorption sensing platform for separation and enrichment of acetone in exhaled breath (EB). UiO-66-NH2@PEI can realize the selective adsorption, desorption and separation for acetone in EB. The flameless combustion hand-held sensor can sensitively respond to acetone in the range of 0.2–12.0 ppm and it can achieve the accurate determination of acetone content in EB of diabetic patients. The hand-held sensor is inexpensive, lightweight and easy to operate, which has great application prospects for the widespread use for diabetic patients in family life.

Label-Free SERS and MD Analysis of Biomarkers for Rapid Point-of-Care Sensors Detecting Head and Neck Cancer and Infections.

For the progress of point-of-care medicine, where individual health status can be easily and quickly monitored using a handheld sensor, saliva serves as one of the best-suited body fluids thanks to its availability and abundance of physiological indicators. Salivary biomarkers, combined with rapid and highly sensitive detection tools, may pave the way to new real-time health monitoring and personalized preventative therapy branches using saliva as a target matrix. Saliva is increasing in importance in liquid biopsy, a non-invasive approach that helps physicians diagnose and characterize specific diseases in patients. Here, we propose a proof-of-concept study combining the unique specificity in biomolecular recognition provided by surface-enhanced Raman spectroscopy (SERS) in combination with molecular dynamics (MD) simulations, which give leave to explore the biomolecular absorption mechanism on nanoparticle surfaces, in order to verify the traceability of two validated salivary indicators, i.e., interleukin-8 (IL-8) and lysozyme (LYZ), implicated in oropharyngeal squamous cell carcinoma (OSCC) and oral infection. This strategy simultaneously assures the detection and interpretation of protein biomarkers in saliva, ultimately opening a new route for the evolution of fast and accurate point-of-care SERS-based sensors of interest in precision medicine diagnostics.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.