Environmental Light Conditions Research Articles

Effect of light bias on male mating signal and female mate choice in a sexually dimorphic Amazon fish

Colourful signals are usually honest indicators of mate quality since they are energetically costly. However, how colours are perceived by choosers is highly affected by the environmental light condition. Amazon black waters are strongly red-biased while clear waters show no apparent colour bias. The sailfin tetra Crenuchus spilurus is a sexually dimorphic Amazon fish species; males have hyperallometric dorsal and anal fins conspicuously ornamented with red and yellow markings. The species has two main lineages, which inhabit black and clear waters. A comparison of the red colouration of the ornaments of males from different lineages indicates that red bias increases the perceived intensity of red colouration but decreases the perceived among-individual variation in red colour. In mate choice experiments, females from all lineages preferred males with larger ornaments. Clear water lineage females were more likely to accept males under red-biased lighting, which increases the apparent red colouration, suggesting the importance of the red colouration in their mate choice. On the other hand, male acceptance by females from black waters did not change under different light conditions, suggesting that signals other than the red colouration (e.g. size of ornaments) were more important in their mate choice.

Medium-Energy Proton Detector Onboard the FY-4B Satellite

This work introduces the instrument design of the medium-energy proton detector (MEPD, detection range: 30 keV–5 MeV) mounted on the Chinese Fengyun-4B (FY-4B) satellite. Compared to a similar detector on the Fengyun-3E (FY-3E) satellite, this instrument has undergone significant changes due to the different orbital radiation environment and solar lighting conditions. Based on the calculation of the radiation model AP8, the geometrical factor is reduced to 0.002 cm2sr, while that of the MEPD on the FY-3E satellite is 0.005 cm2sr. Another difference is that the sensors in some directions are exposed to direct sunlight for 80 min every day on this orbit, depending on the attitude angle of the satellite, which is much worse than that on the FY-3E satellite. According to the calculation results of transmittance of photons through different materials, a 100 nm thickness nickel film is added in front of the sensors to eliminate light pollution completely. The test using a solar simulator shows that the measure is effective and the detector has no error count when the solar irradiance coefficient is 1.0. In addition, the Geant4 software is applied to simulate the particle transportation process under complete machine condition to check the contamination of electrons in the sensors in all directions after magnetic deflection. The data obtained in orbit show that the instrument works properly, and the data are in good agreement with the AP8 model. The observations of the MEPD on board the FY-4B satellite can provide important support for the safety of spacecraft and theoretical research related to space weather.

Leaf and wood functional traits explain the strategies developed by Byrsonima sericea (Malpighiaceae) to survive in Atlantic Forest ecosystems under water and light variations

The Atlantic Forest is a heterogeneous biome comprised of different phytophysiognomies with contrasting microclimatic and edaphic characteristics, such as sandbanks and forests. Sandbanks are ecosystems with low water availability and high irradiance, requiring leaf and wood specific adjustments from species to survive. On the other hand, tropical rain forests lack a pronounced dry season, providing enough resources to allow competition from light-demanding trees and sustaining a spectrum of shade tolerance for understory species. To better understand how functional traits are determined, it is necessary to evaluate variables that are site-specific, i.e. that are displayed within an organism in different environments. We aimed to assess how does variation in the functional traits of the leaf and wood of Byrsonima sericea influence the survival abilities of this species to thrive under contrasting scenarios of light and water availability. Also, which set of morpho-anatomical and physiological traits are considered to be site-specific responses that are affecting positively the capacity of the species to adapt in these contrasting scenarios of the Atlantic Forest? The high irradiance in sandbank shaped functional traits of B. sericea leaves that allowed an allocation strategy of the available resources, with investment in leaf mass rather than in trunk biomechanics and hydraulic efficiency. The higher leaf density through smaller and numerous cells along with higher stomatal density and area, facilitating CO2 diffusion, were associated with a greater photosynthetic capacity and greater intrinsic water use efficiency. As in the forest, light is a limiting factor for species to grow, the fluctuating light over a short timescale can influence whole plant performance, thus requiring an investment in trunk biomechanics. Additionally, a set of wood traits in the forest was related to water availability favoring wider vessel elements with thicker walls, increasing the theoretical hydraulic conductivity, even though it implicated a higher vulnerability to cavitation. Our study has demonstrated that variation of traits of different plant structures, such as the leaf and wood, within a species has direct implications in the survival strategies at each environmental conditions of light and water availability. It is also stated that there are different sets of functional traits which are responsible for allocation strategies, leading to specific trade-offs that allow the species to display site-specific responses in order to survive.

Acute Circadian Disruption Due to Constant Light Promotes Caspase 1 Activation in the Mouse Hippocampus.

In mammals, the circadian system controls various physiological processes to maintain metabolism, behavior, and immune function during a daily 24 h cycle. Although driven by a cell-autonomous core clock in the hypothalamus, rhythmic activities are entrained to external cues, such as environmental lighting conditions. Exposure to artificial light at night (ALAN) can cause circadian disruption and thus is linked to an increased occurrence of civilization diseases in modern society. Moreover, alterations of circadian rhythms and dysregulation of immune responses, including inflammasome activation, are common attributes of neurodegenerative diseases, including Alzheimer', Parkinson's, and Huntington's disease. Although there is evidence that the inflammasome in the hippocampus is activated by stress, the direct effect of circadian disruption on inflammasome activation remains poorly understood. In the present study, we aimed to analyze whether exposure to constant light (LL) affects inflammasome activation in the mouse hippocampus. In addition to decreased circadian power and reduced locomotor activity, we found cleaved caspase 1 significantly elevated in the hippocampus of mice exposed to LL. However, we did not find hallmarks of inflammasome priming or cleavage of pro-interleukins. These findings suggest that acute circadian disruption leads to an assembled "ready to start" inflammasome, which may turn the brain more vulnerable to additional aversive stimuli.

Drivers of contrasting boreal understory vegetation in coniferous and broadleaf deciduous alternative states

AbstractAlternative states defined by tree‐canopy dominance result in different ecosystem functioning and shape habitat conditions for the understory vegetation. One example in the boreal forest is the alternation between broadleaf deciduous and coniferous forests. Disturbances related to natural fires and human land uses have produced changes in tree‐canopy dominance in the boreal region where coniferous forests change to broadleaved forests, affecting understory community dynamics and their related ecosystem processes and functions. To analyze the factors driving changes in understory vegetation and the resistance of its vegetation to shifts between alternative states, we compared the effects of changes in the system between two contrasting boreal forest types (black spruce vs. trembling aspen) in adjacent stands with similar topoedaphic conditions. We performed a 5‐year in situ experiment using alternative states as a theoretical framework including two approaches: (1) the ecosystem approach, manipulating environmental conditions of light, litter, and nutrients in each forest type to determine the main mechanisms associated with tree‐canopy dominance that affect the diversity and composition of understory communities; and (2) the community approach, physically exchanging understory communities between alternative states, to determine their resistance under a new tree‐canopy dominance through time, as well as the resilience of the forest understory after a small‐scale disturbance. Results indicate that the understory vegetation of trembling aspen forests were resistant through time both after changes in local conditions in the ecosystem approach and in the new black spruce‐dominated alternative state in the community approach. In contrast, mosses and ericaceous plants that typically dominate the forest floor of black spruce forests were negatively affected by the physical effect of broadleaf litter addition in our ecosystem approach and they were not resistant when transplanted to trembling aspen forests in the community approach, as they decreased in abundance and were invaded by aspen understory community species over time. The understory vegetation is a key forest ecosystem driver that can contribute to maintain the resilience of the boreal system and help to preserve their ecosystem services, which is a key aspect to consider in forest management faced with the effects of climate change.

Agreement between Two Devices for Measuring Pupil Diameter in Patients Implanted with Multifocal Intraocular Lenses.

The purpose of this study was to evaluate the agreement between three methods for measuring pupil size in patients implanted with multifocal intraocular lenses (MIOLs): Keratograph 5M (K5M), Pentacam AXL Wave (PW), and a simple hand ruler. Sixty-nine subjects implanted with MIOLs and measured at the three-month follow-up visit were included in this retrospective analysis. K5M and PW were used to measure the photopic (PP) and mesopic (MP) pupil sizes, and a hand ruler was used to measure the pupil under environmental light conditions (135 lux). The Bland-Altman method with its limits (LoAs) was used to assess the agreement. The median PP was 2.8, 2.95, and 3 mm for K5M, PW, and the ruler, respectively (p < 0.05). Differences in PP were statistically significant for all paired comparisons (p < 0.0005) except between PW and the ruler (p = 0.44). The LoAs for the difference in PP between K5M and PW was 0.63 mm. The mean difference for MP between K5M and PW was 0.04 mm (p = 0.34) with LoAs of 0.72 mm. MP measured with K5M and PW could be considered interchangeable, although a correction of -0.3 mm (IC95%: -0.23 to -0.39) should be applied to PP measured with PW to attain the K5M mean.

The Interplay between Different Stimuli in a 4D Printed Photo-, Thermal-, and Water-Responsive Liquid Crystal Elastomer Actuator.

Multi-stimuli responsivity in 3D-printed objects is receiving much attention. However, the simultaneous interplay between different environmental stimuli is largely unexplored. In this work, we demonstrate direct ink writing of an oligomeric ink containing an azobenzene photo-switch with an accessible hydrogen bond allowing triple responsivity to light, heat, and water. The resulting printed liquid crystal elastomer performs multiple actuations, the specific response depending on the environmental conditions. Bilayer films formed by printing on a static substrate can rapidly change shape, bending almost 80 degrees if irradiated in air or undergoing a shrinkage of about 50 % of its length when heated. The bilayer film assumes dramatically different shapes in water depending on combined environmental temperature and lighting conditions.

Physiological Effect of Extended Photoperiod and Green Wavelength on the Pituitary Hormone, Sex Hormone and Stress Response in Chub Mackerel, Scomber japonicus

Chub mackerel, Scomber japonicus, is heavily farmed and harvested due to its demand as a high-quality protein source rich in fatty acids. However, the effects of environmental cues on sexual maturation of the fish remain understudied. We aim to elucidate the effect of light manipulation on the hormones related to reproduction and on the stress response in the species. Mackerel were exposed to different photoperiods (12 h light:12 h dark or 14 h light:10 h dark) and light wavelengths (provided by white fluorescent bulbs or green LEDs). Total RNA extracted from the brain was assayed with quantitative polymerase chain reaction (a powerful technique for advancing functional genomics) and blood plasma was analyzed via immunoassay using ELISA kits. The mRNA expression of gene-encoding gonadotropin-releasing hormone, gonadotropin hormone, follicle-stimulating hormone, and luteinizing hormone were significantly increased through the use of an extended photoperiod and green wavelength, which also increased testosterone and 17β-estradiol plasma levels. Plasma levels of cortisol and glucose, which are indicators of a stress response, were significantly decreased through green LED exposure. Our results indicate that environmental light conditions affect the production of pituitary and sex hormones, and reduce the stress response in S. japonicus.

Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach.

Leaf structure plays an important role in photosynthesis. However, the causal relationship and the quantitative importance of any single structural parameter to the overall photosynthetic performance of a leaf remains open to debate. In this paper, we report on a mechanistic model, eLeaf, which successfully captures rice leaf photosynthetic performance under varying environmental conditions of light and CO2 . We developed a 3D reaction-diffusion model for leaf photosynthesis parameterised using a range of imaging data and biochemical measurements from plants grown under ambient and elevated CO2 and then interrogated the model to quantify the importance of these elements. The model successfully captured leaf-level photosynthetic performance in rice. Photosynthetic metabolism underpinned the majority of the increased carbon assimilation rate observed under elevated CO2 levels, with a range of structural elements making positive and negative contributions. Mesophyll porosity could be varied without any major outcome on photosynthetic performance, providing a theoretical underpinning for experimental data. eLeaf allows quantitative analysis of the influence of morphological and biochemical properties on leaf photosynthesis. The analysis highlights a degree of leaf structural plasticity with respect to photosynthesis of significance in the context of attempts to improve crop photosynthesis.

A Self-Adaptive Multiple Exposure Image Fusion Method for Highly Reflective Surface Measurements

Fringe projection profilometry (FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring objects with highly reflective surfaces or high dynamic range surfaces remains challenging when using FPP. A number of multiple exposure image fusion methods have been proposed and successfully improved measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by practical experiences or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness with regard to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two areas of improvement relating to adaptively optimizing the initial exposure and the exposure sequence. First, by introducing the theory of information entropy, combined with an analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the basis of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are provided. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method via the measurement of objects with differences in surface reflectivity under different ambient lighting conditions.

Leveraging deep feature learning for wearable sensors based handwritten character recognition

Despite rapid advancements in technology, handwritten characters still hold significant roles in various fields, including education, communication, biometric signature verification, and health care. These applications often require digitization of the handwritten characters and associated hand movements to facilitate effective analysis and interpretation of the underlying task. Offline and online handwriting recognition are crucial steps involving the digitization of handwritten characters. Most of these existing systems actively use image processing techniques that are highly sensitive to environmental lighting conditions. Surface Electromyography signals (sEMG), being invariant to lighting conditions, are used in online handwriting recognition to facilitate the automatic transcription of handwritten characters. In this article, we have leveraged deep representation learning to build an efficient and robust sEMG-based Handwritten Character Recognition (HCR) pipeline. A Stacked sparse denoising autoencoder network is applied to obtain an effective deep feature representation. These rich low dimensional features obtained are further introduced into basic classifiers, producing state-of-the-art accuracy for the task. Additional experiments were performed to analyze the effect of the fusion of complementary sensing modules (Accelerometer and Gyroscope) on the performance of sEMG based HCR pipeline. Extensive evaluations were performed to ensure the validity of the obtained results. For the experimentation, new datasets consisting of sEMG, Accelerometer, and Gyroscope signals corresponding to 26 handwritten lower English alphabets were collected from 15 subjects. Our proposed pipeline can be used to build real-time Human–Computer Interaction(HCI) applications for smart classrooms facilitating digitization of handwritten notes and clinical applications involving handwriting analysis tasks.

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information.

Recognition of Aircraft Activities at Airports on Video Micro-Satellites: Methodology and Experimental Validation

The remote sensing satellite constellation based on micro-satellites is an important means to construct a global and all-sky earth observation system in the future. Therefore, realizing the recognition of aircraft activities on video micro-satellites is a key technology that needs to be solved urgently. In this paper, an efficient algorithm for aircraft activity recognition that can be deployed on video micro-satellites was proposed. First, aircraft detection was performed on the first incoming remote sensing image using a robust DCNN-based object detection model. Then, a multi-target tracking model incorporating geospatial information was built for aircraft tracking and activity recognition. The algorithm was deployed on an embedded AI computer which was a COTS component. The algorithm was verified using remote sensing videos from commercial micro-satellites. Experimental results show that the algorithm can process aircraft targets of different sizes, and is equally effective even with complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning, entering, and exiting. Based on aircraft tracking results and geospatial information, the motion speed of each aircraft can be obtained, and its activity can be divided into parking, taxiing, or flying. The scheme proposed in this paper has good application prospects in the realization of on-orbit event recognition in micro-satellites with limited computing and memory resources.

Daytime Light Deficiency Leads to Sex- and Brain Region-Specific Neuroinflammatory Responses in a Diurnal Rodent.

Seasonal changes in peripheral inflammation are well documented in both humans and animal models, but seasonal changes in neuroinflammation, especially the impact of seasonal lighting environment on neuroinflammation remain unclear. To address this question, the present study examined the effects of environmental lighting conditions on neuroinflammation in a diurnal rodent model, Nile grass rats (Arvicanthis niloticus). Male and female grass rats were housed in either bright (brLD) or dim (dimLD)light during the day to simulate a summer or winter light condition, respectively. After 4weeks, microglia markers Iba-1 and CD11b, as well as pro-inflammatory cytokines TNF-α and IL-6, were examined in the anterior cingulate cortex (ACC), basolateral amygdala (BLA), and dorsal hippocampus (dHipp). The results revealed that winter-like dim light during the day leads to indicators of increased neuroinflammation in a brain site- and sex-specific manner. Specifically,relatively few changes in the neuroinflammatory markers were observed in the ACC, whilenumerous changes werefound in the BLA and dHipp. In the BLA, winter-like dimLD resulted in hyper-ramified microglia morphology and increased expression of the pro-inflammatory cytokine IL-6, but only in males. In the dHipp, dimLD led to a higher number and hyper-ramified morphology of microglia as well as increased expression of CD11b and TNF-α, but only in females. Neuroinflammatory state is thus influenced by environmental light, differently in males and females, and could play a role in sex differences in the prevalence and symptoms of psychiatric or neurological disorders that are influenced by season or otherenvironmental light conditions. Diurnal Nile grass rats were housed under bright or dim light during the day for 4weeks, simulating seasonal fluctuations in daytime lighting environment. Dim light housing resulted in hyper-ramified morphology of microglia (scale bar, 15μm) and altered expression of pro-inflammatory cytokines (TNF-α) in a sex- and brain region-specific manner.

The Use of Optical Coherence Tomography to Demonstrate Dark and Light Adaptation in a Live Moth.

To work effectively, the eyes of nocturnal insects have a problem they must overcome. During the night, the light levels are low, so their eyes need to be very sensitive; but they also need a way of adapting to environmental light conditions, and protecting those sensitive organs, if a bright light is encountered. Human eyes have a pupil that changes size to regulate light input to the eye. Moths (Lepidoptera) use a light absorbing pigment that moves position to limit the light within the eye. This pigment migration is difficult to record because it is a dynamic process and will only occur in a live moth. This paper presents the first use of Ocular Coherence Tomography as a method of viewing anatomical detail in a compound eye. This is noninvasive and does not harm the insect. To demonstrate the effectiveness, this article documents the dynamic process of light adaptation within a moth’s eye.

Role of serine/threonine protein kinase STN7 in the formation of two distinct photosystem I supercomplexes in Physcomitrium patens.

Reversible thylakoid protein phosphorylation provides most flowering plants with dynamic acclimation to short-term changes in environmental light conditions. Here, through generating Serine/Threonine protein kinase 7 (STN7)-depleted mutants in the moss Physcomitrella (Physcomitrium patens), we identified phosphorylation targets of STN7 kinase and their roles in short- and long-term acclimation of the moss to changing light conditions. Biochemical and mass spectrometry analyses revealed STN7-dependent phosphorylation of N-terminal Thr in specific Light-Harvesting Complex II (LHCII) trimer subunits (LHCBM2 and LHCBM4/8) and provided evidence that phospho-LHCBM accumulation is responsible for the assembly of two distinct Photosystem I (PSI) supercomplexes (SCs), both of which are largely absent in STN7-depleted mutants. Besides the canonical state transition complex (PSI-LHCI-LHCII), we isolated the larger moss-specific PSI-Large (PSI-LHCI-LHCB9-LHCII) from stroma-exposed thylakoids. Unlike PSI-LHCI-LHCII, PSI-Large did not demonstrate short-term dynamics for balancing the distribution of excitation energy between PSII and PSI. Instead, PSI-Large contributed to a more stable increase in PSI antenna size in Physcomitrella, except under prolonged high irradiance. Additionally, the STN7-depleted mutants revealed altered light-dependent phosphorylation of a monomeric antenna protein, LHCB6, whose phosphorylation displayed a complex regulation by multiple kinases. Collectively, the unique phosphorylation plasticity and dynamics of Physcomitrella monomeric LHCB6 and trimeric LHCBM isoforms, together with the presence of PSI SCs with different antenna sizes and responsiveness to light changes, reflect the evolutionary position of mosses between green algae and vascular plants, yet with clear moss-specific features emphasizing their adaptation to terrestrial low-light environments.

Diverse image enhancer for complex underexposed image

The visual appearance of images changes in line with varying environmental light conditions. Adjusting the exposure of various distorted images is a highly complex process. Previous approaches have addressed this issue from different viewpoints and attained remarkable progress. However, they either failed to achieve visually pleasing results or were suitable for a single class of images (e.g., underexposed or nonuniform images). To fully consider the exposure in various distorted images, we proposed a diverse image enhancement model that improved the brightness and contrast, processed the colors, and eliminated the hazy effect. Accordingly, an input red green blue color image was transformed into a hue, saturation, value color image. The V component was inverted and enhanced using three steps. In the first step, the hyperbolic and statistical methods were applied, and then their results were combined using an adjusted logarithmic methodology. This method properly adjusted the high-contrast and low-contrast impact while preserving the vital image information. In the second step, the output of the first step was inverted back and fed into a complete optimization algorithm to estimate the illumination map. Then, the exposure ratio map was estimated using an illumination map, which was adjusted using the camera response function. In the third step, a nonlinear stretching function was introduced to control brightness and contrast. For instance, a lower value of α yielded maximum stretching, and a higher value of α eliminated haze in the image to a great extent. Finally, an empirical evaluation and comparison of the most recent state-of-the-art approaches on eight datasets revealed that the proposed model efficiently addressed the exposure in various degraded images.

Study protocol for measuring the impact of (quasi-)monochromatic light on post-awakening cortisol secretion under controlled laboratory conditions.

Cortisol secretion has a fundamental role in human circadian regulation. The cortisol awakening response (CAR) can be observed as a daily recurring sharp increase in cortisol concentration within the first hour after awakening and is influenced by environmental light conditions. The current work provides the study protocol for an ongoing research project that is intended to explore the spectral dependencies and to discuss measures of emotional state and cognitive functioning potentially related to the CAR. Based on a controlled within-subjects sleep laboratory study, the impact of a two-hour, (quasi-)monochromatic, post-awakening light exposure of different peak wavelength (applied from 6:00 to 8:00 am) on resulting CAR levels should be investigated in a systematic manner to eventually derive a corresponding spectral sensitivity model. As a secondary outcome, it should be explored whether a potentially light-enhanced cortisol secretion might also impact different measures of sleepiness, mood, and vigilance for certain wavelengths. The study protocol described in the present work discusses the various protocol steps using pilot data collected for two different wavelength settings (i.e., short-wavelength blue-light at λmax = 476 nm and long-wavelength red-light at λmax = 649 nm) experienced by a group of four healthy male adults at an average ± SD age of 25.25 ± 3.59 years.

Adaptive Real-Time Object Detection for Autonomous Driving Systems.

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.

Inorganic electrochromic transistors as environmentally adaptable photodetectors

Visual sensing involves the ability to extract critical information from the environment such as colors, shapes and sizes. While conventional photodetectors have demonstrated high photoresponsivity across various wavelengths, they do not display intrinsic adaptability to a dynamic environment. A distinctive feature of our eyes is how they adapt to different light conditions and switch in priority between performance parameters – color sensitivity during the day and high contrast sensitivity during the night. Here we demonstrate an electrochromic MoO3 based transistor which behaves as a photodetector capable of adapting to the environmental light conditions. The ability to sense is enabled by the transduction of optical information into electrical signals via photoconduction. The ability to adapt is enabled by the switching between two phases with distinct optoelectronic properties based on electrochromism. We leverage the coupling between electrochromic switching of MoO3 and photo-electrical transduction to implement sensitization and desensitization of color detection. Subsequently, adaptation characteristics such as photopic and scotopic vision is demonstrated. Eschewing the need for bulky optics and central processing, the proposed electrochromic transistor offers significant advantages in miniaturization of adaptable photodetectors.