Diffuse Component Research Articles

Identification of crossover flux in VRFB cells during battery cycling

When developing dynamical models of vanadium redox flow batteries (VRFBs), it is important to find a trade-off between simplicity, convenience, and model accuracy. Crossover can contribute significantly to the dynamics of such batteries, especially when the number of charge-discharge cycles is large. In this work, we propose a crossover flux modeling approach that takes into account the membrane properties associated with its preparation and operation, when detailed information about its physical characteristics is not available. The proposed approach showed good agreement with the experimental data over 25 cycles with an average error of less than 2 %. A detailed analysis of the contribution of different crossover components revealed that the main influence on the observed capacity drop is related to the diffusion component, which dominates over migration and convection across all cycles, presenting more than 60 % of the losses. In addition, the results showed that migration and convection “mitigate” the influence of diffusion during long-term cycling, thereby reducing the capacity drop. As a result, the proposed approach can be used to analyze the effects of different types of crossover on the capacity decay, which provides researchers and engineers with important information for improving the design and operating conditions of VRFBs to ensure their reliable and fail-safe operation under long-term cycling.

In-office Bleaching Activated With Violet LED: Effect on Pulpal and Tooth Temperature and Pulp Viability.

This study evaluated the influence of hydrogen peroxide (HP) with or without titanium dioxide nanotubes (TiO2) associated with violet LED (VL) regarding: a) the temperature change in the pulp chamber and facial surface; b) the decomposition of HP; and c) the cytotoxicity of the gels on pulp cells. The experimental groups were: HP35 (35% HP/Whiteness HP, FGM); HP35+VL; HP35T (HP35+TiO2); HP35T+VL; HP7 (7.5% HP/White Class 7.5%, FGM); HP7+VL; HP7T (HP7+TiO2); and HP7T+VL. TiO2 was incorporated into the bleaching gels at 1%. Eighty bovine incisors were evaluated to determine temperature change in 8 experimental groups (n=10/group). A k-type thermocouple was used to evaluate the temperatures of the facial surface and in the pulp chamber, achieved by enabling endodontic access to the palatal surface, throughout the 30-minute session. HP decomposition (n=3) of gels was evaluated by using an automatic potentiometric titrator at the initial and 30-minute time points. Trans-enamel and trans-dentinal cell viability were assessed with a pulp chamber device as well as enamel and dentin discs (n=6), and the treatment extracts (culture medium + diffused components) were collected and applied to MDPC-23 odontoblast cells to evaluate cell viability according to the MTT test. A temperature increase in the pulp chamber was observed in the presence of VL at 30 minutes (p<0.05) (Mann-Whitney test). Also at 30 minutes, HP35 showed greater decomposition in the presence of VL rather than in its absence (p<0.05) (mixed linear models and the Tukey-Kramer test). HP7 provided greater cell viability than the groups treated with HP35 (p<0.05) (generalized linear models test). Cell viability was significantly lower for HP7 in the presence of VL (p<0.05). Pulpal temperature increased with VL (maximum of 1.9°C), but did not exceed the critical limit to cause pulp damage. Less concentrated HP resulted in higher cell viability, even when associated with VL.

Detecting clear‐sky periods from photovoltaic power measurements

AbstractA method for detecting clear‐sky periods from photovoltaic (PV) power measurements is presented and validated. It uses five tests dealing with parameters characterizing the connections between the measured PV power and the corresponding clear‐sky power. To estimate clear‐sky PV power, a PV model has been designed using as inputs downwelling shortwave irradiance and its direct and diffuse components received at ground level under clear‐sky conditions as well as reflectivity of the Earth's surface and extraterrestrial irradiance, altogether provided by the McClear service. In addition to McClear products, the PV model requires wind speed and temperature as inputs taken from ECMWF twentieth century reanalysis ERA5 products. The performance of the proposed method has been assessed and validated by visual inspection and compared to two well‐known algorithms identifying clear‐sky periods with broadband global and diffuse irradiance measurements on a horizontal surface. The assessment was carried out at two stations located in Finland offering collocated 1‐min PV power and broadband irradiance measurements. Overall, total agreement ranges between 84% and 97% (depending on the season) in discriminating clear‐sky and cloudy periods with respect to the two well‐known algorithms serving as reference. The disagreement fluctuating between 6% and 15%, depending on the season, primarily occurs while the PV module temperature is adequately high and/or when the sun is close to the horizon with many more interactions between the radiation, the atmosphere and the ground surface.

Energy-conserving neural network for turbulence closure modeling

In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the ‘discretize first, filter next’ approach. In this approach we apply a spatial averaging filter to existing fine-grid discretizations. The main novelty is that we introduce an additional set of equations which dynamically model the energy of the subgrid scales. Having an estimate of the energy of the subgrid scales, we can use the concept of energy conservation to derive stability. The subgrid energy containing variables is determined via a data-driven technique. The closure model is used to model the interaction between the filtered quantities and the subgrid energy. Therefore the total energy should be conserved. Abiding by this conservation law yields guaranteed stability of the system. In this work, we propose a novel skew-symmetric convolutional neural network architecture that satisfies this law. The result is that stability is guaranteed, independent of the weights and biases of the network. Importantly, as our framework allows for energy exchange between resolved and subgrid scales it can model backscatter. To model dissipative systems (e.g. viscous flows), the framework is extended with a diffusive component. The introduced neural network architecture is constructed such that it also satisfies momentum conservation. We apply the new methodology to both the viscous Burgers' equation and the Korteweg-De Vries equation in 1D. The novel architecture displays superior stability properties when compared to a vanilla convolutional neural network.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. Included were 71 glioma patients (mean age, 50.17 ± 13.38years; 35 men). HM-MRI images were collected at five different echo times (80-200ms) with seven b-values (0-3000s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). HM-MRI can aid in non-invasive diagnosis of gliomas.

S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the multi-scale normalized cross correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

On viscous stratified Darcy–Forchheimer flow in a horizontal porous layer with thermal anisotropy and variable permeability

This study analyzes the effect of anisotropy and the internal heat source in a Darcy–Forchheimer porous layer. It is well known that the variations in viscosity can be attributed to the temperature. Therefore, in the present problem, we consider a linear variation in viscosity with temperature for simplicity. We first derived the linear instability theory and then established global stability using the energy functional approach. In the global stability analysis, we show that working with the L2 norm fails to give a sufficient condition for global stability by exhibiting that the associated maximization problem is unbounded in the underlying stability measure space. Then, we show that a conditional stability bound can be achieved by restricting the internal heat source parameter Q with higher-order norms. The eigenvalue problems obtained in linear and nonlinear theories were integrated numerically. The linear and nonlinear instability thresholds are then compared to identify the potential regions of sub-critical instabilities. It is observed that the system is stabilized when the horizontal component of thermal diffusivity dominates and is unstable when the vertical component of thermal diffusivity dominates. We also found that increasing the variable permeability parameter λ destabilized the system. It is observed that increasing viscosity stabilizes the system, and decreasing viscosity encourages the start of convection. It is also interesting that, in the presence of an internal heat source, the region of subcritical instability increases with increasing viscosity effect but reduces with increasing vertical permeability λ.

Characterizing the Gamma-Ray Emission Properties of the Globular Cluster M5 with the Fermi-LAT

We analyzed the globular cluster M5 (NGC 5904) using 15 yr of gamma-ray data from the Fermi Large Area Telescope (LAT). Using rotation ephemerides generated from Arecibo and FAST radio telescope observations, we searched for gamma-ray pulsations from the seven millisecond pulsars (MSPs) identified in M5. We detected no significant pulsations from any of the individual pulsars. In addition, we searched for possible variations of the gamma-ray emission as a function of orbital phase for all six MSPs in binary systems, but we did not detect any significant modulations. The gamma-ray emission from the direction of M5 is well described by an exponentially cutoff power-law spectral model, although other models cannot be excluded. The phase-averaged emission is consistent with being steady on a timescale of a few months. We estimate the number of MSPs in M5 to be between 1 and 10, using the gamma-ray conversion efficiencies for well-characterized gamma-ray MSPs in the Third Fermi-LAT Catalog of Gamma-ray Pulsars, suggesting that the sample of known MSPs in M5 is (nearly) complete, even if it is not currently possible to rule out a diffuse component of the observed gamma rays from the cluster.

Absorption of collimated incident radiation in a semitransparent strongly scattering medium: Computational analysis and explanation of red algae blooming in snow

The study of directional radiation absorption in weakly absorbing and strongly scattering media is of interest for a variety of important applications, including laser hyperthermia of human tumors in a therapeutic window of transparency, determination of the optical and thermal properties of highly porous ceramics, and calculations of the deep solar heating of snow cover in polar regions and mountains. In such problems, multiple scattering of radiation can lead to a non-monotonic variation of the absorbed radiation power with distance from the illuminated surface of the medium and to the appearance of the absorption maximum at some depth below the surface. This effect has a known physical explanation and can be predicted using the suggested analytical solution based on the transport approximation for the scattering phase function and the two-flux approximation for the diffuse component of the radiation intensity. The range of illumination angles at which the absorption maximum inside an optically thick medium is observed is determined. The position and magnitude of this maximum are also obtained. In the present work, the verification of this approximate solution using reference numerical calculations is carried out for the first time. It is shown that the simple analytical model is acceptable for regular calculations. Computational results for pure snow illuminated by the Sun showed that red light gives the strongest absorption maximum at a small distance from the snow surface. This is a basis of the presented physical explanation for the intense photosynthesis of red microalgae responsible for the well-known ``snow blood'' phenomenon.

Spatial frequency domain imaging combining profile correction enables accurate real-time quantitative mapping of optical properties of apples

Spatial frequency domain imaging technology enables non-contact, wide-field quantitative analysis of optical properties in strong turbid media, thereby enhancing bruise detection in fruits based on changes in optical property values. However, precise and real-time measurement of the optical properties of curved biological materials is challenging due to the significant influence of tissue contours. This paper primarily presents a research study on real-time correction method based on the property that DC component diffuse reflectance (Rd_DC) and AC component diffuse reflectance (Rd_AC) are influenced by the contour trend of the tissue in a similar manner. The aim of this method is to eliminate the significant impact of sample contour angle on demodulation and enhance the detection of apple bruise. Sinusoidal illumination at a spatial frequency of 0.2 mm−1 was projected at 730 nm, with the experiments carried out on standard reflector, polytetrafluoroethylene sphere, and non-bruised and bruised apples. The results showed that, the relative error of the planar standard reflector decreased from the maximum value of 52.85–4.74%. The median of the Rd_AC for the polytetrafluoroethylene spherical standard, which fluctuated between 0.6 and 0.8, was corrected to the theoretical value of 1. For normal apples, the maximum fluctuation in the standard deviation of Rd_AC, after median filtering, reduced from 172.94% at 300×300 pixels to 18.69%. The boxplot of Rd_AC for bruised apples became smaller, with the lower fence being corrected from nearly 0–0.1750, which closely matched the theoretical value of 0.17. Additionally, the correlation of reduced scattering coefficient (μs′) in the ideal region of interest (100×100 pixels) remained at 0.9785 before and after correction, while other areas were corrected, making the bruising more pronounced. The correction method did not require additional experiments, with the consumed time in millisecond level.

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.

Gas exchange abnormalities in Long COVID are driven by the alteration of the vascular component.

There are uncertainties whether the impairment of lung diffusing capacity in COVID-19 is due to an alteration in the diffusive conductance of the alveolar membrane (Dm), or an alteration of the alveolar capillary volume (Vc), or a combination of both. The combined measurement DLNO and DLCO diffusion, owing to NO higher affinity and faster reaction rate with haemoglobin compared to CO, enables the simultaneous and rapid determination of both Vc and Dm. The aim of the present study was to better identify the precise cause of post-COVID-19 diffusion impairment. Using the combined NO and CO gas transfer techniques (DLNO and DLCO), it is possible to better understand whether gas exchange abnormalities are due to membrane or alveolar capillary volume components. The present study was aimed at evaluating pulmonary gas exchange one year after severe COVID-19. Results: The cohort included 33 survivors to severe COVID-19 (median age 67 years, 70% male) with no pre-existing lung disease, who underwent clinical, lung function and imaging assessments at 12 months due to persistence of respiratory symptoms or radiological impairment. The gas exchange abnormalities were mainly determined by the compromise of the vascular component as demonstrated by vascular pattern of gas exchange impairment (i.e., DLNO/DLCO≥110%, 76% of the sample), and by a reduction of the Vc (73%), while the Dm was reduced only in 9% of the entire sample. We did not find a correlation between the gas exchange impairment and the extent of the chest CT alterations (DLCO p = 0.059 and DLNO p = 0.054), which on average were found to be mild (11% of the parenchyma). In COVID-19 survivors who are still symptomatic or have minimal CT findings at one year, gas exchange abnormalities are determined by impairment of the vascular component, rather than the diffusive component of the alveolar membrane.

LOFAR detection of extended emission around a mini halo in the galaxy cluster Abell 1413

Context. The relation between giant radio halos and mini halos in galaxy clusters is not understood. The former are usually associated with merging clusters, while the latter are found in relaxed systems. In recent years, the advent of low-frequency radio observations has challenged this dichotomy by finding intermediate objects with a hybrid radio morphology. Aims. We aim to investigate the presence of diffuse radio emission in the cluster Abell 1413 and determine its dynamical status to explore the relation between mini halos and giant radio halos. Methods. We used LOFAR observations centred at 144 MHz to study the diffuse radio emission. To investigate the dynamical state of the system, we used newly analysed XMM-Newton archival data. Abell 1413 shows features that are typically present in both relaxed (e.g., peaked X-ray surface brightness distribution and some large-scale inhomogeneities) and disturbed (e.g., flatter temperature and metallicity profiles) clusters. Results. This suggests that Abell 1413 is neither disturbed nor fully relaxed, and we argue that it is an intermediate-phase cluster. At 144 MHz, we discover a wider diffuse component surrounding the previously known mini halo at the cluster center. By fitting the radio surface-brightness profile with a double-exponential model, we can disentangle the two components. We find an inner mini halo with an e-folding radius, re, 1 = 28 ± 5 kpc, and an extended component with re, 2 = 290 ± 60 kpc. We also evaluated the point-to-point correlation between the radio and X-ray surface brightness, finding a sublinear relation for the outer emission and a superlinear relation for the mini halo. The mini halo and the diffuse emission extend over different scales and show different features, confirming the double nature of the radio emission and suggesting that the mechanisms responsible for the re-acceleration of the radio-emitting particle might be different.

Toward Optimal Fitting Parameters for Multi-Exponential DWI Image Analysis of the Human Kidney: A Simulation Study Comparing Different Fitting Algorithms

In DWI, multi-exponential signal analysis can be used to determine signal underlying diffusion components. However, the approach is very complex due to the inherent low SNR, the limited number of signal decay data points, and the absence of appropriate acquisition parameters and standardized analysis methods. Within the scope of this work, different methods for multi-exponential analysis of the diffusion signal in the kidney were compared. To assess the impact of fitting parameters, a simulation was conducted comparing the free non-negative (NNLS) and rigid non-linear least square (NLLS) fitting methods. The simulation demonstrated improved accuracy for NNLS in combination with area-under-curve estimation. Furthermore, the accuracy and stability of the results were further enhanced utilizing optimized parameters, namely 350 logarithmically spaced diffusion coefficients within [0.7, 300] × 10−3 mm2/s and a minimal SNR of 100. The NNLS approach shows an improvement over the rigid NLLS method. This becomes apparent not only in terms of accuracy and omitting prior knowledge, but also in better representation of renal tissue physiology. By employing the determined fitting parameters, it is expected that more stable and reliable results for diffusion imaging in the kidney can be achieved. This might enable more accurate DWI results for clinical utilization.

An image encryption algorithm based on multi-layered chaotic maps and its security analysis

This study introduces a novel symmetric image encryption algorithm that employs a multi-layered architecture of chaotic maps, incorporating both confusion and diffusion components. The selected chaotic maps, namely Aizawa, Ricker, Sine-Circle, and Chirikov, have been well-established as effective sources of chaos in the existing literature. Within the proposed algorithm, these chaotic maps are strategically utilised in distinct layers to enhance the security of the encrypted image. The algorithm demonstrates applicability to real-world scenarios, distinguished by its simplicity, high-speed performance, and low computational complexity. The efficacy of the method is underlined by its dependence on both original images and secret keys, rendering it resilient against brute force and differential attacks. Rigorous experimentation, encompassing correlation, histogram analysis, NBCR, NPCR, UACI, PSNR, and NIST values, establishes the proposed encryption algorithm's robustness and security against diverse attack modalities, including differential and statistical approaches. These findings position the algorithm as a compelling solution for safeguarding sensitive information in various practical applications.

Boltzmann Equation and Knudsen Group - Boundary Shape and Boundary Conditions

A Boltzmann type equation is considered where both, the physical and the velocity space, are bounded. It is assumed that the boundary conditions consist of a reflective as well as a diffusive component. Existence of positive bounds from below and above has been proved. It has been demonstrated that under conditions on the shape of the boundary, the underlying Knudsen type transport semigroup can be embedded in a not necessarily positive group for time t ∈ ℝ. As a consequence, the Boltzmann type equation considered has a unique global solution for time t ∈ [τ 0, ∞) for some τ 0 < 0. The time τ 0 does not depend on the initial value at time 0. It can be arbitrarily small depending on the intensity of the collisions.

Soft X-Ray Energy Spectra in the Wide-field Galactic Disk Area Revealed with HaloSat

We analyzed data from HaloSat observations for five fields in the Galactic disk located far away from the Galactic center (135° < l < 254°) to understand the nature of soft X-ray energy emission in the Galactic disk. The fields have 14° diameter and were selected to contain no significant high-flux X-ray sources. All five HaloSat soft X-ray energy spectra (0.4–7 keV with energy resolution of < 100 eV below 1 keV) show a possibility of the presence of unresolved high-temperature plasma in the Galactic disk (UHTPGD) with a temperature of 0.8–1.0 keV and an emission measure of (8–11) × 10−4 cm−6 pc in addition to the soft X-ray diffuse background components mainly studied at higher Galactic latitudes (solar wind charge exchange emission, Local Hot Bubble, Milky Way halo emission, and the cosmic X-ray background). This suggests that the UHTPGD is present across the whole Galactic disk. We also observed UHTPGD emission in a region with no bright sources in an XMM-Newton field contained within one of the HaloSat fields. The temperature and emission measure are consistent with those measured with HaloSat. Moreover, the stacked spectra of the X-ray pointlike sources and near-infrared-identified point sources such as stars in the XMM-Newton field also show a spectral feature similar to the UHTPGD emission. This suggests that the UHTPGD may partly originate from pointlike sources such as stars.

MHz to TeV expectations from scotogenic WIMP dark matter

ABSTRACT The indirect search for dark matter is typically restricted to individual photon bands and instruments. In the context of multiwavelength observations, finding a weak signal in large foreground and background at only one wavelength band is hampered by systematic uncertainties dominating the signal strength. Dark matter particle annihilation is producing Standard Model particles of which the prompt photon emission is searched for in many studies. However, also the secondary emission of charged particles from dark matter annihilation in the TeV range results in comparable or even stronger fluxes in the GHz–GeV range. In this study, we calculate the prompt and secondary emission of a scotogenic weakly interacting massive particle (WIMP) with a mass of 1 TeV in 27 dwarf galaxies of the Milky Way. For the secondary emission, we include inverse Compton scattering, bremsstrahlung, and synchrotron radiation, which results in a ‘triple hump’ structure characteristic for only dark matter and no other astrophysical source. In order to determine the best candidates for multi-instrument analyses, we estimate the diffuse emission component of the Milky Way itself, including its own dark matter halo from the same scotogenic WIMP model. We find signal-to-background ratios of individual sources on the order of 10−3 to 10−2 across X-ray to γ-ray assuming J factors for the cold dark matter distribution inferred from observations and no additional boosting due to small-scale clumping. We argue that a joint multiwavelength analysis of all nearby galaxies and the extension towards the cosmic gamma-ray background is required to disentangle possible dark matter signals from astrophysical background and foreground.

Learning physically based material and lighting decompositions for face editing

Lighting is crucial for portrait photography, yet the complex interactions between the skin and incident light are expensive to model computationally in graphics and difficult to reconstruct analytically via computer vision. Alternatively, to allow fast and controllable reflectance and lighting editing, we developed a physically based decomposition through deep learned priors from path-traced portrait images. Previous approaches that used simplified material models or low-frequency or low-dynamic-range lighting struggled to model specular reflections or relight directly without intermediate decomposition. However, we estimate the surface normal, skin albedo and roughness, and high-frequency HDRI maps, and propose an architecture to estimate both diffuse and specular reflectance components. In our experiments, we show that this approach can represent the true appearance function more effectively than simpler baseline methods, leading to better generalization and higher-quality editing.

Effects of surface geometry on light exposure, photoacclimation and photosynthetic energy acquisition in zooxanthellate corals.

Symbiotic corals display a great array of morphologies, each of which has unique effects on light interception and the photosynthetic performance of in hospite zooxanthellae. Changes in light availability elicit photoacclimation responses to optimize the energy balances in primary producers, extensively documented for corals exposed to contrasting light regimes along depth gradients. Yet, response variation driven by coral colony geometry and its energetic implications on colonies with contrasting morphologies remain largely unknown. In this study, we assessed the effect of the inclination angle of coral surface on light availability, short- and long-term photoacclimation responses, and potential photosynthetic usable energy. Increasing surface inclination angle resulted in an order of magnitude reduction of light availability, following a linear relationship explained by the cosine law and relative changes in the direct and diffuse components of irradiance. The light gradient induced by surface geometry triggered photoacclimation responses comparable to those observed along depth gradients: changes in the quantum yield of photosystem II, photosynthetic parameters, and optical properties and pigmentation of the coral tissue. Differences in light availability and photoacclimation driven by surface inclination led to contrasting energetic performance. Horizontally and vertically oriented coral surfaces experienced the largest reductions in photosynthetic usable energy as a result of excessive irradiance and light-limiting conditions, respectively. This pattern is predicted to change with depth or local water optical properties. Our study concludes that colony geometry plays an essential role in shaping the energy balance and determining the light niche of zooxanthellate corals.