MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to make voxel-accurate segmentations of the region of interest. However, the labels required to train these CNNs are time-consuming to generate and require attention from subject matter experts to ensure quality. For tasks where voxel-level precision is not required, object detection models offer a viable alternative that can reduce annotation effort. Despite this potential application, there are few options for general-purpose object detection frameworks available for 3-D medical imaging. We report on MedYOLO, a 3-D object detection framework using the one-shot detection method of the YOLO family of models and designed for use with medical imaging. We tested this model on four different datasets: BRaTS, LIDC, an abdominal organ Computed tomography (CT) dataset, and an ECG-gated heart CT dataset. We found our models achieve high performance on a diverse range of structures even without hyperparameter tuning, reaching mean average precision (mAP) at intersection over union (IoU) 0.5 of 0.861 on BRaTS, 0.715 on the abdominal CT dataset, and 0.995 on the heart CT dataset. However, the models struggle with some structures, failing to converge on LIDC resulting in a mAP@0.5 of 0.0.

Selecting iron and zinc nano-fertilizers for rice traits in drought by statistical analysis.

Optimum use of fertilizer plays an important role in increasing the performance of traits in rice and other agricultural products. The use of nano-fertilizers can be very important in the optimal use of fertilizer and in increasing the quantity and quality of agricultural products. To investigate the effect of iron and zinc nano-fertilizers on the physiological and morphological characteristics of rice plants (Tarom Hashemi variety) under drought stress conditions, a split plot experiment was carried out in the form of a randomized complete block design in three replications in Mazandaran region. The results of the combined analysis indicated that the effect of iron nano-fertilizer and zinc nano-fertilizer has a significant difference at the level of 0.01 and 0.05 in terms of all the evaluated traits. In the means comparison, it was concluded that normal humidity conditions can have a positive effect on the performance of the traits, but in the S1 stress conditions (drought stress at the rate of 50% of normal irrigation), the traits showed a good performance trend. Furthermore, increasing the amount of iron nano-fertilizer at the F2 level (Iron sulphate 100 kg/ha + 1 foliar spraying of iron chelate 2%) can be very effective in the growth of yield and the desirability of traits. In examining the amount of zinc nano-fertilizer consumption on the traits, showed the most positive performance on the traits at the level of Z2 (Use of 5 mg of zinc nano-fertilizer per kg of experiment soil), but with the increase to the level of Z3 (Use 7.5 mg of zinc nano-fertilizer per kg of experiment soil), this favorability was greatly reduced. The result of the correlation coefficients between the traits and the correlation diagram also showed a positive and significant correlation between the traits. In the graphical analysis, treatments tr33, tr8, tr19, tr24, tr7, tr6, and tr2 were selected as desirable treatments from the polygon graph. Treatments tr19, tr9 and tr6 were recognized as favourable treatments in terms of the treatment ranking diagram in terms of all traits. Based on the treatment selection diagram based on ideal treatment, tr19, tr9 and tr6 treatments were identified as desirable treatments. In general, it is possible to use tr19 (S1×F1×Z2), tr6 (N×F1×Z1) and tr9 (N×F2×Z0) treatments as treatments with high efficiency and power to increase the performance of traits and optimal use of nano-fertilizers in rice cultivation.

Enhancement of solar evacuated tube unit filled with nanofluid implementing three lobed storage unit equipped with fins.

This study discusses an evacuated tube collector-type solar water heater (ETCSWH) using a phase change material (PCM) chamber with fins, nanofluid, and nano-enhanced phase change material (NEPCM). First, the charging phenomena in a horizontal triplex tube heat exchanger (TTHX) equipped with fins, natural convection, and an ETCSWH system without PCM is simulated to validate the solution. The impact of adding fins and nanoparticles with a volume fraction of 3% of Al2O3 and Cu to paraffin wax and water-based fluid, respectively, on the unit's efficiency has been examined. The proposed system for the PCM melting process, heat storage, fluid flow behavior in the system, and velocity distribution and temperature contour in the storage tank and three parts of the absorber tube have been evaluated using ANSYS FLUENT software in a three-dimensional and transient simulation. The results show that Case 8 has improved by 39.7% compared to Case 1 and Case 4 by 5.2% compared to Case 1 within 4 h of the melting process. Also, Case 8 with a 43% and 6.4% shorter melting time than Cases 1 and 5 has the best performance and the greatest heat transfer rate. The productivity of the ETCSWH system is considerably enhanced by the use of fins, NEPCM, and nanofluid.

Self-stigma, religiosity, and perceived social support in people with recent-onset psychosis in the Islamic Republic of Iran: Associations with symptom severity and psychosocial functioning.

AIMS: Most evidence on psychosocial factors in recent-onset psychosis comes from high-income countries in Europe, Australia, Canada and the USA, while these factors are likely to differ under varying sociocultural and economic circumstances. In this study, we aimed to investigate associations of self-stigma, religiosity and perceived social support with symptom severity and psychosocial functioning in an Iranian cohort of people with recent-onset psychosis (i.e. illness duration of <2 years). METHODS: We used baseline data of 361 participants (N = 286 [74%] male, mean age = 34 years [Standard Deviation = 10.0]) from the Iranian Azeri Recent-onset Acute Phase Psychosis Survey (ARAS). We included assessments of self-stigma (Internalized Stigma of Mental Illness, ISMI), religiosity (based on Stark & Glock), perceived social support (Multidimensional Scale of Perceived Social Support, MSPSS), symptom severity (Positive And Negative Syndrome Scale, PANSS) and psychosocial functioning (clinician-rated Global Assessment of Functioning Scale, GAF, and self-reported World Health Organization Disability Assessment Schedule 2.0, WHODAS 2.0). Descriptive analyses were employed to characterize the study sample. Covariate-adjusted ordinal and multivariable linear regression analyses were performed to investigate cross-sectional associations of baseline ISMI, religiosity and MSPSS with concurrent PANSS, GAF and WHODAS 2.0. RESULTS: Higher self-stigma was associated with poorer self-reported functioning (B = 0.375 [95% Confidence Interval (CI): 0.186, 0.564]) and more severe concurrent symptoms (B = 0.436 [95% CI: 0.275, 0.597]). Being more religious was associated with poorer clinician-rated functioning (OR = 0.967 [95% CI: 0.944, 0.991]), but with less severe symptoms (B = -0.258 [95% CI: -0.427, -0.088]). Stronger social support was associated with poorer clinician-rated (OR = 0.956 [95% CI: 0.935, 0.978]) and self-reported functioning (B = 0.337 [95% CI: 0.168, 0.507]). CONCLUSION: This study shows that self-stigma, religiosity and perceived social support were associated with symptom severity and clinician-rated as well as self-reported psychosocial functioning in an Iranian cohort of people with recent-onset psychosis. The findings extend previous evidence on these psychosocial factors to one of the largest countries in the Middle East, and suggest that it may be worthwhile to develop strategies aimed at tackling stigma around psychosis and integrate the role of religiosity and social support in mental ill-health prevention and therapy.

Spatial Precision Tailoring the Catalytic Activity of Graphene Monolayers for Designing Janus Swimmers.

Graphene monolayers have interesting applications in many fields due to their intrinsic physicochemical properties, especially when they can be postmodified with high precision. Herein, we describe the highly site-selective functionalization of freestanding graphene monolayers with platinum (Pt) clusters by bipolar electrochemistry. The deposition of such metal spots leads to catalytically active hybrid two-dimensional (2D) nanomaterials. Their catalytic functionality is illustrated by the spatially controlled decomposition of hydrogen peroxide, inducing motion at the water/air interface due to oxygen bubble evolution. A series of such 2D Janus structures with Pt deposition at predefined positions (corners and edges) is studied with respect to the generation of autonomous motion. The type and speed of motion can be fine-tuned by controlling the deposition time and location of the Pt clusters. These proof-of-principle experiments indicate that this type of hybrid 2D object opens up interesting perspectives in terms of applications, such as environmental detection or remediation.

Low-Profile, Large-Range Compressive Strain Sensing Using Micromanufactured CNT Micropillar Arrays.

Tactile sensors, or sensors that collect measurements through touch, have versatile applications in a wide range of fields including robotic gripping, intelligent manufacturing, and biomedical technology. Hoping to match the ability of human hands to sense physical changes in objects through touch, engineers have experimented with a variety of materials from soft polymers to hard ceramics, but so far, all have fallen short. A grand challenge for developers of "human-like" bionic tactile sensors is to be able to sense a wide range of strains while maintaining the low profile necessary for compact integration. Here, we developed a low-profile tactile sensor (∼300 µm in height) based on patterned, vertically aligned carbon nanotubes (PVACNT) that can repetitively sense compressive strains of up to 75%. Upon compression, reversible changes occur in the points of contact between CNTs, producing measurable changes in electrical admittance. By patterning VACNT pillars with different aspect ratios and pitch sizes, we engineered the range and resolution of strain sensing, suggesting that CNT-based tactile sensors can be integrated according to device specifications.

A machine-learning approach to human ex vivo lung perfusion predicts transplantation outcomes and promotes organ utilization.

Ex vivo lung perfusion (EVLP) is a data-intensive platform used for the assessment of isolated lungs outside the body for transplantation; however, the integration of artificial intelligence to rapidly interpret the large constellation of clinical data generated during ex vivo assessment remains an unmet need. We developed a machine-learning model, termed InsighTx, to predict post-transplant outcomes using n = 725 EVLP cases. InsighTx model AUROC (area under the receiver operating characteristic curve) was 79 ± 3%, 75 ± 4%, and 85 ± 3% in training and independent test datasets, respectively. Excellent performance was observed in predicting unsuitable lungs for transplantation (AUROC: 90 ± 4%) and transplants with good outcomes (AUROC: 80 ± 4%). In a retrospective and blinded implementation study by EVLP specialists at our institution, InsighTx increased the likelihood of transplanting suitable donor lungs [odds ratio=13; 95% CI:4-45] and decreased the likelihood of transplanting unsuitable donor lungs [odds ratio=0.4; 95%CI:0.16-0.98]. Herein, we provide strong rationale for the adoption of machine-learning algorithms to optimize EVLP assessments and show that InsighTx could potentially lead to a safe increase in transplantation rates.

Selection of maize hybrids based on genotype × yield × trait (GYT) in different environments.

This study aimed to identify the best genotypes using the genotype × yield × trait (GYT) method. To investigate the relationships was performed between yield × traits in four regions of Karaj, Birjand, Shiraz and Arak in two cropping years in a randomized complete block design (RCBD) with three replications. The average grain yield in four regions and two years of the experiment was calculated as 5966 kg/ha, and GYT was obtained based on the multiplication of grain yield with different traits. Comparing the average effect of genotype × year in different environments showed that KSC703 and KSC707 hybrids are among the most productive hybrids among the studied genotypes in grain yield. By examining the correlation coefficients between yield × traits in the tested areas, Y × TWG with Y × GW, Y × NRE, Y × NGR and Y × EL, Y × ED with Y × NGR, Y × NRE with Y × GW and the combination of Y × GW with Y × GL had a positive and significant correlation in all regions. The correlation diagrams were drawn on the evaluated areas' data and showed the correlation of most compounds except Y × GT with each other. Based on the analysis of the main components, the first three components explained the greatest diversity in the population. They were named the component ear grain profile, grain thickness component and plant height profile component.

The application of non-uniform magnetic field for thermal enhancement of the nanofluid flow inside the U-turn pipe at solar collectors.

The improvement of heat transfer inside the solar heat exchangers is important for the development of solar energy in an urban area. In this study, the usage of a non-uniform magnetic field on the thermal efficiency of the nanofluid (Fe3O4) streaming inside the U-turn pipe of solar heat exchangers is examined. Computational fluid dynamic is applied to visualize the nanofluid flow inside the solar heat exchanger. The role of magnetic intensity and Reynolds number on thermal efficiency are fully investigated. The effect of single and triple sources of the magnetic field is also studied in our research. Obtained results indicate that the usage of the magnetic field results in the production of vortex in the base fluid and heat transfer improves inside the domain. Our finding indicates that the usage of the magnetic field with Mn = 25 K would improve the average heat transfer by about 21% along the U-turn pipe of solar heat exchangers.

An X-FEM technique for numerical simulation of variable-density flow in fractured porous media.

Solute transport is one of the major topics in geological studies. Fracture is a significant characteristic of natural porous media, where the solute can transport due to its higher density with respect to the density of fluid. As the solute migrates in the medium, the density of the fluid changes with time. In this paper, the mass transport problem in the fractured porous media is modeled using the extended finite element method (X-FEM). An advection-diffusion equation is adopted to define the transport phenomenon in conjunction with the continuity equation of fluid. Transport regimes including diffusion, dispersion and advection are taken into the computational model. The presence of fractures within a porous medium substantially affects the transport behavior. In order to resolve the issue of discontinuity in the field variables, the X-FEM is implemented to discretize the discontinuity of medium. The Newmark integration scheme is adopted to discretize the governing equations in time domain. The nonlinear equations are solved by the Newton-Raphson iterative technique in a fully coupled manner. Finally, in order to illustrate the performance of the proposed computational model, two conventional problems, including the Schincariol problem and the Elder problem as well as the fractured Elder problem are solved numerically. Different patterns of fractures including horizontal and vertical intersecting cracks are adopted to study the effect of fracture density as well as the capability and versatility of the proposed computational model. The method is described in details and the pitfalls of the whole approach are demonstrated. •The density-driven fluid flow in naturally fractured porous media is modeled using an enhanced-FEM technique.•The effect of fractures (faults) in the porous medium is investigated by modeling the transport of saltwater in the fractured Elder problem.•The proposed computational model provides an accurate prediction of subsurface hydrology for a field-scale closed desert basin.

A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning.

The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository [1].

Water-constrained green development framework based on economically-allocable water resources.

Water as a main driver for sustainable development (SD) should be optimally allocated to different users to support economic, social, and environmental functions. Traditional approaches are not able to account for all the mentioned functions simultaneously, therefore a change in the allocation approaches is necessary. This paper proposes a new framework for inter-sectoral water allocation called "water-constrained green development" (WCGD) to better meet the SD goals. The framework optimally allocates economically-allocable water (EAW), which is the total available water resources left after subtracting the amount of water required for drinking, sanitation, and environment (DSE), to different job classes. It was tested in Sistan Region- a low-developed area in southeast of Iran- which stands on agriculture. In the recent years, because of water crisis, intensity of dust problem, lack of sustained occupation, and immigration, the region's rate of population growth has been negative. Also, due to decrease of Helmand River inflow, Hamoun wetland, being the major source of food and shelter for the Sistan's residents, has been degraded. Therefore, Sistan Region needs to take a new development route. The shares of occupation and gross domestic product (GDP) in the agricultural sector of Sistan are respectively 29.1 and 14.8%, whereas they are on average 1 and 7% in Iran. Application of the proposed framework in Sistan Region under three scenarios of available EAW resources showed that the optimal reallocation of water among 15 job classes can improve job availability and GDP of the region currently suffering from poor economy and employment conditions. Based on the optimal job pattern obtained, the share of GDP of Sistan's agricultural sector drops to 7.1% while the shares of industrial and service sectors increase respectively from 9.7 and 75.4% to 13.7 and 79.2%, which are close to those of the country averages. Also, under the WCGD-based optimal solution, 68, 14, and 18% of people will respectively be employed in service, industry, and agriculture sectors. Additionally, the total available jobs and GDP will increase by 8.9 and 51.1%, respectively, leading to improved socio-economic well-being of  the region's people and protection of its environmental resources.

Stability yield indices on different sweet corn hybrids based on AMMI analysis.

Currently, sweet corn is considered an important crop due to its high sugar content and low starch content. Important sugars in sweet corn include sucrose, fructose, glucose, and maltose. The purpose of the present study was to use the yield indices of the eight examined sweet corn hybrids and the correlation of the yield indices together. Concentration is important for consumers in terms of yield indices. The research site was located at the Látókép Experimental Station of the University of Debrecen. The small plot experiment had a strip plot design with four replications. The previous crop was sweet corn; the plant density was 64 thousand/ha. The obtained result indicates that Biplot AMMI based on IPCA1 showed that the DB, NO, GS, and GB hybrids had stability and high performance in terms of yield indices. At the same time, fructose and glucose had stable parameters for the hybrids involved in the study. IPCA1 AMMI biplot showed that the ME hybrid had stability and high performance in terms of iron and zinc as well. IPCA2 AMMI biplot showed that DE, GB, and GS hybrids had stability and the highest performance on yield parameters in the scope of the research. Fructose, glucose, and sucrose had stable parameters on hybrids based on IPCA2. The DB and SE hybrids had desirable performance in Lutein and Zeaxanthin based on the biplot. The DE hybrid had a maximum performance on iron and zinc parameters.

Computational modeling of multiple myeloma interactions with resident bone marrow cells.

The interaction of multiple myeloma with bone marrow resident cells plays a key role in tumor progression and the development of drug resistance. The tumor cell response involves contact-mediated and paracrine interactions. The heterogeneity of myeloma cells and bone marrow cells makes it difficult to reproduce this environment in in-vitro experiments. The use of in-silico established tools can help to understand these complex problems. In this article, we present a computational model based on the finite element method to define the interactions of multiple myeloma cells with resident bone marrow cells. This model includes cell migration, which is controlled by stress-strain equilibrium, and cell processes such as proliferation, differentiation, and apoptosis. A series of computational experiments were performed to validate the proposed model. Cell proliferation by the growth factor IGF-1 is studied for different concentrations ranging from 0-10 ng/mL. Cell motility is studied for different concentrations of VEGF and fibronectin in the range of 0-100 ng/mL. Finally, cells were simulated under a combination of IGF-1 and VEGF stimuli whose concentrations are considered to be dependent on the cancer-associated fibroblasts in the extracellular matrix. Results show a good agreement with previous in-vitro results. Multiple myeloma growth and migration are shown to correlate linearly to the IGF-1 stimuli. These stimuli are coupled with the mechanical environment, which also improves cell growth. Moreover, cell migration depends on the fiber and VEGF concentration in the extracellular matrix. Finally, our computational model shows myeloma cells trigger mesenchymal stem cells to differentiate into cancer-associated fibroblasts, in a dose-dependent manner.

Pulse distortion caused by waveguide inhomogeneity in nonlinear optical wavelength converters.

Low-noise integrated all-optical wavelength converters that can be operated in short pulse regime are essential tools to overcome contention resolution in a modern communication network, based on wavelength division multiplexing. Any imperfect functionality in such devices causes non-ideal optical power transfer to the converted data pulses. All imperfections during the preparation and operation of the wavelength converters can be addressed to the waveguide inhomogeneity which distorts data pulses to be converted. This paper reports different waveguide inhomogeneity effects on the pulse distortion while using periodically poled lithium niobate waveguide as wavelength converters. Three types of [Formula: see text]-based nonlinear optical processes, including second harmonic generation, difference frequency generation, and cascaded second harmonic generation/difference frequency generation are numerically studied to show that any constant, linear, and quadratic waveguide inhomogeneity causes short pulse (down to 1 ns) distortion in such wavelength converters. In addition, it is shown that the reconstruction of [Formula: see text]-shaped generated pulses is possible, when suitable upside-down quadratic variations of obtained inhomogeneity are deliberately induced in the waveguide. Notably, for pulsed second harmonic generation, the generated pulse can be compressed using an upside-down quadratic phase mismatch.

On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis.

One of the greatest environmental risks in the cement industry is particulate matter emission (i.e., PM2.5 and PM10). This paper aims to develop descriptive-analytical solutions for increasing the accuracy of predicting particulate matter emissions using resample data of Kerman cement plant. Photometer instruments DUST TRAK and BS-EN-12341 method were used to determine concentration of PM2.5 and PM10. Sampling was performed on 4 environmental stations of Kerman cement plant in the four seasons. In order to accurate assessment of particulate matter concentration, a new model was proposed to resample cement plant time series data using Pandas in Python. The effect of meteorological parameters including wind speed, relative humidity, air temperature and rainfall on the particulate matter concentration was investigated through statistical analysis. The results indicated that the maximum annual average of 24-h of PM2.5 belonged to the east side (opposite the clinker depot) in 2019 (31.50 µg m-3) and west side (in front of the mine) in 2020 (31.00 µg m-3). Also, maximum annual average of 24-h of PM10 belonged to the west side (in front of the mine) in 2020 (121.00 µg m-3) and east side (opposite the clinker depot) in 2020 (120.75 µg m-3). The PM2.5 and PM10 concentrations are more than the allowable limit. The results demonstrate that particulate matter concentration increases with increasing relative humidity and rainfall. Finally, the SARIMA model was used to predict the particulate matter concentration. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04645-3.

Relations of Adolescent Knowledge of COVID-19, Social Media Engagement, and Experiences During Quarantine/Lockdown with Well-Being.

This study investigated the relations of adolescent COVID-19 knowledge, quarantine/lockdown experiences, and social media use with indices of their psychosocial adjustment. The sample consisted of 215 adolescents from throughout the United States, with adolescents ranging from ages 14 to 17. Better knowledge of COVID-19 was related to lower loneliness, stress, anxiety, depression, and fear of missing out (FoMO). Higher parent-reported restrictions during quarantine were associated with these difficulties as well. Further, the lowest anxiety was reported for adolescents with good COVID-19 knowledge who also checked social media relatively less frequently. The findings point to the importance of accurate information about COVID-19 for adolescents and the impact of quarantine/lockdown experiences on their perceived emotional and social adjustment.

Antibacterial Properties of Bacteriocin Purified from Serratia marcescens and Computerized Assessment of its Interaction with Antigen 43 in Escherichia coli.

Bacteriocins are a kind of antimicrobial peptides that kill or inhibit the growth of bacterial strains. The purpose of this study was to investigate the antibacterial effect of Serratia marcescens on several pathogenic bacterial strains. Bacteriocin produced by S. marcescens was purified by chromatography with Sephadex G-75 column, and its antibacterial effect on gram-negative bacteria, including Escherichia coli ATCC 700928, Pseudomonas aeruginosa PTCC 1707, S. marcescens PTCC 1621, Vibrio fischeri PTCC 1693, and Vibrio harveyi PTCC 1755, were evaluated by the disk diffusion method. The structure of bacteriocin was determined by nuclear magnetic resonance spectroscopy. The interaction of bacteriocin with the antigen 43 (Ag43) of E. coli was evaluated by the molecular docking method. Bacteriocin extracted from bacterial isolates had antibacterial activity on E. coli strains but not on other studied strains. Bioinformatics analysis also showed bacteriocin docking with Ag43 with an energy of -159.968 kJ/mol. Natural compounds, such as bacteriocin, can be an alternative to common chemical compounds and antibiotics. To reach a definite conclusion in this regard, there is a need for further research and understanding of their mechanism of action.

Wireless electrochemical light emission in ultrathin 2D nanoconfinements.

Spatial confinement of chemical reactions or physical effects may lead to original phenomena and new properties. Here, the generation of electrochemiluminescence (ECL) in confined free-standing 2D spaces, exemplified by surfactant-based air bubbles is reported. For this, the ultrathin walls of the bubbles (typically in the range of 100-700 nm) are chosen as a host where graphene sheets, acting as bipolar ECL-emitting electrodes, are trapped and dispersed. The proposed system demonstrates that the required potential for the generation of ECL is up to three orders of magnitude smaller compared to conventional systems, due to the nanoconfinement of the potential drop. This proof-of-concept study demonstrates the key advantages of a 2D environment, allowing a wireless activation of ECL at rather low potentials, compatible with (bio)analytical systems.

Wireless Imaging of Transient Redox Activity Based on Bipolar Light-Emitting Electrode Arrays.

Bipolar electrochemistry (BE) is a wireless electrochemical technique, which enables asymmetric electroactivity on the surface of conducting objects. This technique has been extensively studied for different electrochemical applications, including synthesis, separation, sensing, and surface modification. Here, we employ BE for imaging the transient electrochemical activity of different redox species with high accuracy via an array of light-emitting diodes having different lengths. Such a gradient allows the differentiation of redox systems due to their intrinsic difference in thermodynamic potential and the evaluation of their diffusional behavior based on the intensity of light emission. The result is an instantaneous optical readout of analytical information, equivalent to classic electrochemical scanning techniques, such as linear sweep voltammetry.