Investigating the Free Volumes as Nanospaces in Human Stratum Corneum Lipid Bilayers Using Positron Annihilation Lifetime Spectroscopy (PALS).

This work is the first one that provides not only evidence for the existence of free volumes in the human stratum corneum but also focuses on comparing these experimental data, obtained through the unique positron annihilation lifetime spectroscopy (PALS) method, with theoretical values published in earlier works. The mean free volume of 0.269 nm was slightly lower than the theoretical value of 0.4 nm. The lifetime τ3 (1.83 ns with a coefficient of variation CV of 3.21%) is dependent on the size of open sites in the skin. This information was used to calculate the free volume radius R (0.269 nm with CV 2.14%), free volume size Vf (0.081 nm3 with CV 4.69%), and the intensity I3 (9.01% with CV 10.94%) to estimate the relative fractional free volume fv (1.32 a.u. with CV 13.68%) in human skin ex vivo. The relation between the lifetime of o-Ps (τ3) and the radius of free volume (R) was formulated using the Tao-Eldrup model, which assumes spherical voids and applies to sites with radii smaller than 1 nm. The results indicate that PALS is a powerful tool for confirming the existence of free volumes and determining their size. The studies also focused on describing the probable locations of these nanospaces in SC lipid bilayers. According to the theory, these play an essential role in dynamic processes in biological systems, including the diffusion of low-molecular-weight hydrophobic and moderately hydrophilic molecules. The mechanism of their formation has been determined by the molecular dynamics of the lipid chains.

Rapid Determination of Cr3+ and Mn2+ in Water Using Laser-Induced Breakdown Spectroscopy Combined with Filter Paper Modified with Gold Nanoclusters.

Excessive emissions of heavy metals not only cause environmental pollution but also pose a direct threat to human health. Therefore, rapid and accurate detection of heavy metals in the environment is of great significance. Herein, we propose a method based on laser-induced breakdown spectroscopy (LIBS) combined with filter paper modified with bovine serum albumin-protected gold nanoclusters (LIBS-FP-AuNCs) for the rapid and sensitive detection of Cr3+ and Mn2+. The filter paper modified with AuNCs was used to selectively enrich Cr3+ and Mn2+. Combined with the multi-element detection capability of LIBS, this method achieved the simultaneous rapid detection of Cr3+ and Mn2+. Both elements showed linear ranges for concentrations of 10-1000 µg L-1, with limits of detection of 7.5 and 9.0 µg L-1 for Cr3+ and Mn2+, respectively. This method was successfully applied to the determination of Cr3+ and Mn2+ in real water samples, with satisfactory recoveries ranging from 94.6% to 105.1%. This method has potential application in the analysis of heavy metal pollution.

Probing chromatin condensation dynamics in live cells using interferometric scattering correlation spectroscopy.

Chromatin organization and dynamics play important roles in governing the regulation of nuclear processes of biological cells. However, due to the constant diffusive motion of chromatin, examining chromatin nanostructures in living cells has been challenging. In this study, we introduce interferometric scattering correlation spectroscopy (iSCORS) to spatially map nanoscopic chromatin configurations within unlabeled live cell nuclei. This label-free technique captures time-varying linear scattering signals generated by the motion of native chromatin on a millisecond timescale, allowing us to deduce chromatin condensation states. Using iSCORS imaging, we quantitatively examine chromatin dynamics over extended periods, revealing spontaneous fluctuations in chromatin condensation and heterogeneous compaction levels in interphase cells, independent of cell phases. Moreover, we observe changes in iSCORS signals of chromatin upon transcription inhibition, indicating that iSCORS can probe nanoscopic chromatin structures and dynamics associated with transcriptional activities. Our scattering-based optical microscopy, which does not require labeling, serves as a powerful tool for visualizing dynamic chromatin nano-arrangements in live cells. This advancement holds promise for studying chromatin remodeling in various crucial cellular processes, such as stem cell differentiation, mechanotransduction, and DNA repair.

Structural and functional impacts of neonicotinoids analogues on Apis mellifera's chemosensory protein: Insights from spectroscopic and molecular modeling investigations.

In the intricate web of ecological relationships, pollinators such as the Italian honeybee (Apis mellifera) play a crucial role in maintaining biodiversity and agricultural productivity. This study focuses on the interactions between three neonicotinoid compounds and the honeybee's chemosensory protein 3 (CSP3), a key player in their olfactory system. Employing advanced spectroscopic techniques and molecular modeling, we explore the binding dynamics and conformational changes in CSP3 upon exposure to these pesticides. The research reveals that all three neonicotinoids considerably quench CSP3's fluorescence through a dynamic and static mixing mechanism, indicating a strong binding affinity, predominantly driven by hydrophobic interactions. UV-visible absorption, synchronous fluorescence, and 3D fluorescence spectra support slight changes in the microenvironment around the aromatic amino acids of CSP3. Circular dichroism spectra indicate a reduction in CSP3's α-helix content, suggesting structural alterations. Molecular docking and dynamics simulations further elucidate the binding modes and stability of these interactions, highlighting the role of specific amino acids in CSP3's binding cavity. Findings provide critical insights into molecular mechanisms by which neonicotinoids may impair honeybee chemosensory function, offering implications for designing safer pesticides and understanding the broader ecological impact of these chemicals on pollinator health.

Intraoperative characterization of cardiac tissue: the potential of light scattering spectroscopy.

Significance: Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions. Aim: To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization. Approach: A custom LSS probe was used to gather spectral data from locations across 14 excised human pediatric nodal tissue samples (8 sinus nodes, 6 atrioventricular nodes). The LSS spectra were used to train linear and neural-network-based regressor models to predict tissue composition characteristics derived from the 3D models. Results: Nodal tissue region nuclear densities were reported. A linear model trained to regress nuclear density from spectra achieved a prediction r-squared of 0.64 and a concordance correlation coefficient of 0.78. Conclusions: These methods build on previous studies suggesting that LSS measurements combined with machine learning signal processing can provide clinically relevant cardiac tissue composition.

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications.

Background: Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods: In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results: In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions: The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

High-precision spectra captured by a spectral camera and suppression of their nonlinearity.

The high sensitivity of photoplethysmography (PPG) spectral signals provides conditions for extracting dynamic spectra carrying nonlinear information. By the idea of spatial conversion precision, this paper uses a spectral camera to collect highly sensitive spectral data of 24 wavelengths and proposes a method for extracting dynamic spectra of three different optical path lengths and their joint modeling. In the experiment, the models of the red blood cells and white blood cells established by the joint spectra achieved good results, with the correlation coefficients above 0.77. This study has great significance for achieving high-precision noninvasive quantitative analysis of human blood components.

Single-Particle Spectroscopic Chromatography Reveals Heterogeneous RNA Loading and Size Correlations in Lipid Nanoparticles.

Lipid nanoparticles (LNP) have emerged as pivotal delivery vehicles for RNA therapeutics. Previous research and development usually assumed that LNPs are homogeneous in population, loading density, and composition. Such perspectives are difficult to examine due to the lack of suitable tools to characterize these physicochemical properties at the single-nanoparticle level. Here, we report an integrated spectroscopy-chromatography approach as a generalizable strategy to dissect the complexities of multicomponent LNP assembly. Our platform couples cylindrical illumination confocal spectroscopy (CICS) with single-nanoparticle free solution hydrodynamic separation (SN-FSHS) to simultaneously profile population identity, hydrodynamic size, RNA loading levels, and distributions of helper lipid and PEGylated lipid of LNPs at the single-particle level and in a high-throughput manner. Using a benchmark siRNA LNP formulation, we demonstrate the capability of this platform by distinguishing seven distinct LNP populations, quantitatively characterizing size distribution and RNA loading level in wide ranges, and more importantly, resolving composition-size correlations. This SN-FSHS-CICS analysis provides critical insights into a substantial degree of heterogeneity in the packing density of RNA in LNPs and size-dependent loading-size correlations, explained by kinetics-driven assembly mechanisms of RNA LNPs.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow.

Significance: In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. Aim: Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution. Approach: The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80 Hz. Results: Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head. Conclusions: Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400-1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green-red range (550-650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Spectroscopic detection of bioaerosols with the wibs-4+: Anthropogenic and meteorological impacts.

This research builds upon a previous study that explored the potential of the modified WIBS-4+ to selectively differentiate and detect different bioaerosol classes. The current work evaluates the influence of meteorological and air quality parameters on bioaerosol concentrations, specifically pollen and fungal spore dynamics. Temperature was found to be the most influential parameter in terms of pollen production and release, showing a strong positive correlation. Wind data analysis provided insights into the potential geographic origins of pollen and fungal spore concentrations. Fungal spores were primarily shown to originate from a westerly direction, corresponding to agricultural land use, whereas pollen largely originated from a North-easterly direction, corresponding to several forests. The influence of air quality was also analysed to understand its potential impact on the WIBS fluorescent parameters investigated. Most parameters had a negative association with fungal spore concentrations, whereas several anthropogenic influences showed notable positive correlations with daily pollen concentrations. This is attributed to similar driving forces (meteorological parameters) and geographical origins. In addition, the WIBS showed a significant correlation with anthropogenic pollutants originating from combustion sources, suggesting the potential for such modified spectroscopic instruments to be utilized as air quality monitors. By combining all meteorological and pollution data along with WIBS-4+ channel data, a set of Multiple Linear Regression (MLR) analyses were completed. Successful results with R2 values ranging from 0.6 to 0.8 were recorded. The inclusion of meteorological parameters was dependent on the spore or pollen type being examined.

Theory of 2D electronic spectroscopy of water soluble chlorophyll-binding protein (WSCP): Signatures of Chl b derivate.

We investigate how electronic excitations and subsequent dissipative dynamics in the water soluble chlorophyll-binding protein (WSCP) are connected to features in two-dimensional (2D) electronic spectra, thereby comparing results from our theoretical approach with experimental data from the literature. Our calculations rely on third-order response functions, which we derived from a second-order cumulant expansion of the dissipative dynamics involving the partial ordering prescription, assuming a fast vibrational relaxation in the potential energy surfaces of excitons. Depending on whether the WSCP complex containing a tetrameric arrangement of pigments composed of two dimers with weak excitonic coupling between them binds the chlorophyll variant Chl a or Chl b, the resulting linear absorption and circular dichroism spectra and particularly the 2D spectra exhibit substantial differences in line shapes. These differences between Chl a WSCP and Chl b WSCP cannot be explained by the slightly modified excitonic couplings within the two variants. In the case of Chl a WSCP, the assumption of equivalent dimer subunits facilitates a reproduction of substantial features from the experiment by the calculations. In contrast, for Chl b WSCP, we have to assume that the sample, in addition to Chl b dimers, contains a small but distinct fraction of chemically modified Chl b pigments. The existence of such Chl b derivates has been proposed by Pieper et al. [J. Phys. Chem. B 115, 4042 (2011)] based on low-temperature absorption and hole-burning spectroscopy. Here, we provide independent evidence.

Reassociation of Skeletal Remains Using Laser-Induced Breakdown Spectroscopy.

A major challenge in forensic anthropology and bioarcheology is the development of fast and effective methods for sorting commingled remains. This study assesses how portable laser-induced breakdown spectroscopy (LIBS) can be used to group skeletal remains based on their elemental profiles. LIBS spectra were acquired from the remains of 45 modern skeletons, with a total data set of 8388 profiles from 1284 bones. Spectral feature selection was conducted to reduce the spectral profiles to the peaks exhibiting the highest variation among individuals. Emission lines corresponding to 9 elements (Ca, P, C, K, Mg, Na, Al, Ba, and Sr) were found important for classification. Linear discriminant analysis (LDA) was concurrently used to classify each spectral profile. From the 45 individuals, each LIBS spectrum was successfully sorted to its corresponding skeleton with an average accuracy of 87%. These findings indicate that variation exists among the LIBS profiles of individuals' skeletal remains, highlighting the potential for portable LIBS technology to aid in the sorting of commingled remains.

Investigating the mechanistic impact of pork soft tissue preparation techniques on the classification precision of laser-induced breakdown spectroscopy.

The investigation of the mechanism underlying the impact of biological soft tissue sample preparation methods on laser-induced breakdown spectroscopy (LIBS) signals can enhance the stability of LIBS signals. Our study focused on four specific preparation methods applied to pork samples: rapid freezing, fresh slicing, drying, and pressing. The influence of various preparation techniques on the signal-to-noise ratio and fluctuation of Ca, Na, Mg, and CN bands within the sample spectra was assessed. The signal-to-noise ratios for samples that were dried and pressed notably improved. And the pressing method effectively mitigated the uneven distribution of pork tissue components, displaying superior spectral line stability. To explain this phenomenon, we used the Saha-Boltzmann diagram to estimate the plasma temperature. Remarkably, there was a significant reduction in plasma temperature fluctuations across four pressed samples, with a standard deviation of 108.53. Furthermore, we undertook a classification analysis employing support vector machine models to corroborate the generalization efficacy of the sample preparation technique. Dried and pressed samples demonstrated notably higher classification accuracy, precision, and recall (all >93%) compared to frozen and fresh samples, where these metrics remained below 86%. The performance of the SVM model was ultimately evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC). The AUC for the frozen, fresh, dried, and pressed samples was 0.854, 0.907, 0.989, and 0.996, respectively. The findings revealed that the pressing method exhibited superior performance, followed by drying, fresh slicing, and freezing, in descending order of effectiveness.

Choosing a camera and optimizing system parameters for speckle contrast optical spectroscopy.

Speckle contrast optical spectroscopy (SCOS) is an emerging camera-based technique that can measure human cerebral blood flow (CBF) with high signal-to-noise ratio (SNR). At low photon flux levels typically encountered in human CBF measurements, camera noise and nonidealities could significantly impact SCOS measurement SNR and accuracy. Thus, a guide for characterizing, selecting, and optimizing a camera for SCOS measurements is crucial for the development of next-generation optical devices for monitoring human CBF and brain function. Here, we provide such a guide and illustrate it by evaluating three commercially available complementary metal-oxide-semiconductor cameras, considering a variety of factors including linearity, read noise, and quantization distortion. We show that some cameras that are well-suited for general intensity imaging could be challenged in accurately quantifying spatial contrast for SCOS. We then determine the optimal operating parameters for the preferred camera among the three and demonstrate measurement of human CBF with this selected low-cost camera. This work establishes a guideline for characterizing and selecting cameras as well as for determining optimal parameters for SCOS systems.

A low-cost spectroscopic nutrient management system for Microscale Smart Hydroponic system.

Hydroponics offers a promising approach to help alleviate pressure on food security for urban residents. It requires minimal space and uses less resources, but management can be complex. Microscale Smart Hydroponics (MSH) systems leverage IoT systems to simplify hydroponics management for home users. Previous work in nutrient management has produced systems that use expensive sensing methods or utilized lower cost methods at the expense of accuracy. This study presents a novel inexpensive nutrient management system for MSH applications that utilises a novel waterproofed, IoT spectroscopy sensor (AS7265x) in a transflective application. The sensor is submerged in a hydroponic solution to monitor the nutrients and MSH system predicts the of nutrients in the hydroponic solution and recommends an adjustment quantity in mL. A three-phase model building process was carried out resulting in significant MLR models for predicting the mL, with an R2 of 0.997. An experiment evaluated the system's performance using the trained models with a 30-day grow of lettuce in a real-world setting, comparing the results of the management system to a control group. The sensor system successfully adjusted and maintained nutrient levels, resulting in plant growth that outperformed the control group. The results of the models in actual deployment showed a strong, significant correlation of 0.77 with the traditional method of measuring the electrical conductivity of nutrients. This novel nutrient management system has the potential to transform the way nutrients are monitored in hydroponics. By simplifying nutrient management, this system can encourage the adoption of hydroponics, contributing to food security and environmental sustainability.

Cotton seed cultivar identification based on the fusion of spectral and textural features.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

Imaging the elemental distribution within human malignant melanomas using Laser-Induced Breakdown Spectroscopy.

The diagnosis of malignant melanoma, often an inconspicuous but highly aggressive tumor, is most commonly done by histological examination, while additional diagnostic methods on the level of elements and molecules are constantly being developed. Several studies confirmed differences in the chemical composition of healthy and tumor tissue. Our study presents the potential of the LIBS (Laser-Induced-Breakdown Spectroscopy) technique as a diagnostic tool in malignant melanoma (MM) based on the quantitative changes in elemental composition in cancerous tissue. Our patient group included 17 samples of various types of malignant melanoma and one sample of healthy skin tissue as a control. To achieve a clear perception of results, we have selected two biogenic elements (calcium and magnesium), which showed a dissimilar distribution in cancerous tissue from its healthy surroundings. Moreover, we observed indications of different concentrations of these elements in different subtypes of malignant melanoma, a hypothesis that requires confirmation in a more extensive sample set. The information provided by the LIBS Imaging method could potentially be helpful not only in the diagnostics of tumor tissue but also be beneficial in broadening the knowledge about the tumor itself.

Association between atherosclerotic cardiovascular disease score and skin carotenoid levels estimated via refraction spectroscopy in the Japanese population: a cross-sectional study.

Carotenoids play a role in preventing and impeding the progression of atherosclerotic cardiovascular diseases (ASCVDs) through their anti-oxidative effects. This study evaluated associations between ASCVD risk and skin carotenoid (SC) levels, reflecting dietary carotenoid intake. Participants' ASCVD risk was assessed using the Hisayama ASCVD risk prediction model, and SC levels were measured through a reflection spectroscope (Veggie Meter). The associations between high ASCVD risk and SC levels were analyzed using logistic regression analysis and a restricted cubic spline (RCS) model. A total of 1130 men and women (mean age: 56 years) from participants who underwent a health examination in Seirei Center for Health Promotion and Prevention Medicine in 2019 and 2022 were analyzed. Of these, 4.6% had moderate or high ASCVD risk. Mean SC values were 236, 315, 376, 447, and 606 in quintile Q1 to Q5, respectively. The adjusted odds ratios (95% confidence intervals) of SC quintile for moderate- or high-risk ASCVD was 0.24 (0.12-0.51) in Q5 (495 ≤), 0.42 (0.23-0.77) in Q4, 0.50 (0.29-0.88) in Q3, and 0.68 (0.41-1.12) in Q2 compared to Q1 (< 281). High SC values continuously showed non-linear inverse association with moderate- or high-risk for ASCVD in Japanese adults. Non-invasive SC measurements may be a good indicator for recommending carotenoids to prevent cardiovascular disease.

Development of an atmospheric pressure plasma-based OES device for in-situ mapping of Cd and related elements in plants.

We have developed an innovative optical emission spectrometry imaging device integrating a diode laser for sample introduction and an atmospheric pressure plasma based on dielectric barrier discharge for atomization and excitation. By optimizing the device parameters and ensuring appropriate leaf moisture, we achieved effective imaging with a lateral resolution as low as 50 µm. This device allows for tracking the accumulation of Cd and related species such as K, Zn, and O2+∙, in plant leaves exposed to different Cd levels and culture times. The results obtained are comparable to established in-lab imaging and quantitative methods. With its features of compact construction, minimal sample preparation, ease of operation, and low limit of detection (0.04 µg/g for Cd), this novel methodology shows promise as an in-situ elemental imaging tool for interdisciplinary applications.