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Recent Submissions

  • Characterizing the role of the BIRD proteins in Solanum lycopersicum L.

    Farran, Ayman (2022-08-17) [Thesis]
    Advisor: Blilou, Ikram
    Committee members: Merzaban, Jasmeen; Rayapuram, Naganand
    The BIRD protein JACKDAW (JKD) belong to the INDETERMINATE DOMAIN (IDD) protein family shown to regulate many developmental processes in plants. JKD encodes a Zinc Finger Protein expressed in the root ground tissue and regulates root patterning in Arabidopsis thaliana (Arabidopsis). Recent and unpublished study indicates that JKD is involved in plant defense response in Arabidopsis. Here we study the JKD function in tomato plants (Solanum lycopersicum). We analyzed the tomato JKD orthologues (Solyc09g007550 (Solyc09) and Solyc10g084180 (Solyc10)) mutant lines, which were generated by Crispr-Cas and TILLING (Targeting Induced Local Lesions in Genomes). Our data indicate that, like in Arabidopsis, Solyc09 controls root ground tissue patterning; the mutant lines show extra cell division in the inner cortex and disturbed stem cell patterning. In addition, we found that both Solyc09 and Solyc10 control the root and stem thickness and regulate tomato leaf shape. To further investigate whether Solyc09 and Solyc10 have a function in tomato when subjected to biotic stress, we evaluated the mutants response to the necrotrophic fungi Botrytis cinerea. We found that the tomato bird mutants have less infection when compared to the control. Taken together our data show that Solyc09 and Solyc10 genes play an essential role in tomato root, shoot development, and in plant immune response to the pathogenic fungi.
  • Indigenously Developed HD Video Transmission System for UAVs Employing a 3 × 3 MIMO Antenna System

    Akhter, Zubair; Bilal, Rana Muhammad; Telegenov, Kuat; Feron, Eric; Shamim, Atif (IEEE Open Journal of Antennas and Propagation, Institute of Electrical and Electronics Engineers (IEEE), 2022-08-16) [Article]
    Real-time high-definition (HD) video transmission for long distances (.1 km) between an unmanned aerial vehicle (UAV) and a ground station is a challenging problem. The existing real-time solutions are limited to relatively low-quality video streaming, whereas an HD video, which is stored in the local memory, is accessible only when the UAV returns to the ground. In this study, a real-time HD video transmission system (VTS) with a multiple-input multiple-output (MIMO) antenna configuration and state-of-the-art coverage is proposed for security and inspection applications. The proposed VTS employs ultrathin, lightweight antennas that are suitable for seamless integration with a UAVfs body without any protrusion. A 3 × 3 MIMO configuration with large antenna bandwidths (3.9% at 2.4 GHz and 6.9% at 5.2 GHz ) enables the simultaneous transmission of multiple data streams with high data rates (>30 Mbps), and a high antenna gain ( 10 dBi) allows a relatively long communication range (>3 km). In field experiments, the UAV module (comprising thin conformal antennas, embedded electronics, an RF transceiver, and an HD camera) is attached to a commercial drone DJI Matrice 600 Pro. The HD videofs reception performance is investigated for operation in two frequency bands (2.4 and 5.2 GHz) for both horizontal and vertical antenna orientations. The maximum and average data rates for various distances are reported. Based on the conducted field experiments, it is found that the proposed VTS is capable of transmitting real-time HD video up to a 3.56-km distance with a receiver sensitivity of.76 dBm. The maximum achieved data rates at a 500-m distance are 10 and 43 Mbps for operation in the 2.4-and 5.2-GHz frequency bands, respectively.
  • Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks

    Liu, Sixiu; Birnie, Claire Emma; Alkhalifah, Tariq Ali; Bakulin, Andrey (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Blind-spot networks have been shown to be natural noise suppressors under the assumption that noise is unpredictable based on the information fed into the network during training. Trained in a self-supervised manner, such approaches only utilise the original raw data to determine to remove the noise. In this work, we propose two novel elements for enhancing blind-spot denoising: (1) the introduction of a 2-class segmentation task to aid the network in identification of interest areas of signals that require particular attention during denoising, and; (2) the introduction of a trace-wise noise mask designed to obscure the coherency of noise from being observed by the network. The joint scheme is achieved by introducing a joint loss function to balance between the two deep learning tasks. As such, the final joint scheme is the combination of a self-supervised, blind-spot denoising procedure and a supervised segmentation procedure. We illustrate how the joint scheme can improve the denoising performance of the network, hypothesising that this is due to the introduction of prior information guiding the denoising procedure to areas of focus. Preliminary results from synthetic data contaminated by trace-wise noise, show an increase in the structural similarity index from 0.989 to 0.995, when comparing the optimal jointscheme versus the pure denoising procedure. Future work will extend the procedure to field data where rule-based approaches will be used to generate the segmentation labels.
  • A Methodology for Optimizing the Calibration and Validation of Reactive Transport Models for Cement-Based Materials

    Addassi, Mouadh; Marcos-Meson, Victor; Kunther, Wolfgang; Hoteit, Hussein; Michel, Alexander (Materials, MDPI AG, 2022-08-15) [Article]
    Reactive transport models are useful tools in the development of cement-based materials. The output of cement-related reactive transport models is primarily regarded as qualitative and not quantitative, mainly due to limited or missing experimental validation. This paper presents an approach to optimize the calibration process of reactive transport models for cement-based materials, using the results of several short-term experiments. A quantitative comparison of changes in the hydrate phases (measured using TGA and XRD) and exposure solution (measured using ICP-OES) was used to (1) establish a representative chemical model, limiting the number of hydrate phases and dissolved species, and (2) calibrate the transport processes by only modeling the initial tortuosity. A case study comprising the early age carbonation of cement is presented to demonstrate the approach. The results demonstrate that the inclusion of a microstructure model in our framework minimizes the impact of the initial tortuosity factor as a fitting parameter for the transport processes. The proposed approach increases the accuracy of reactive transport models and, thus, allowing for more realistic modeling of long-term exposure.
  • Bayesian RockAVO: Direct petrophysical inversion with hierarchical conditional GANs

    Corrales, Miguel; Izzatullah, Muhammad; Ravasi, Matteo; Hoteit, Hussein (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Reservoir characterization is a critical component in any oil and gas, geothermal, and CO2 sequestration project. A fundamental step in the process of characterizing the subsurface is represented by the inversion of petrophysical parameters from seismic data. However, this problem suffers from various uncertainty sources originating from inaccuracies in the measurements, modeling errors, and complex geological processes. Moreover, the non-linearity of the rock-physics model and Zoeppritz equation that constitute the modelling operator, further complicates the inversion process. In this work, we propose a novel data-driven approach where well-log information is used to obtain optimal basis functions that link band-limited petrophysical reflectivities to pre-stack seismic data. Subsequently, the inversion of such band-limited reflectivities for petrophysical parameters is framed in a Bayesian framework where a generative adversarial network is used to produce a geologically realistic prior distribution. The trained prior distribution is updated using the Stein Variational Gradient Descent and a set of representative solutions is produced that is consistent with the uncertainties in the data and the nonlinear operators.
  • Using deep learning for automatic detection and segmentation of carbonate microtextures

    Birnie, Claire Emma; Chandra, Viswasanthi (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    The difficulties involved in studying micrometer-sized micrite crystals, and quantifying the associated impact on large scale geophysical properties, have long hindered our society’s understanding of both Middle Eastern and global microporous limestones. Instance segmentation procedures, from the field of deep learning, offer the ability to identify at a pixel-level each individual crystal within an SEM image, allowing for automated morphological analysis. We illustrate how the common Masked Region-based Convolution Neural Network from computer vision can be adapted to the task of identifying individual micrite crystal within gray-scale SEM images. Leveraging Transfer Learning, the ResNet50 neural architecture is used with weights initialized through a pre-training on Microsoft’s Common Objects in COntext (COCO) dataset. The resulting model accurately detects and separates a number of crystals observed within different SEM images. However the trained model is also shown to be highly susceptible to noise introduced as part of the imaging procedure, for example charging noise. Future work will aim to make the procedure more robust, reducing the impact of noise by adapting the pre-processing workflow and incorporating more noisy images into the training dataset.
  • Pwd-pinn: Slope-assisted seismic interpolation with physics-informed neural networks

    Brandolin, Francesco; Ravasi, Matteo; Alkhalifah, Tariq Ali (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Seismic data can be expressed as a superposition of local plane waves. A gather of traces can be described by the local plane wave differential equation (PDE), that allows to predict each of the traces from the previous one, given the knowledge of the local slope of the events. In the approach presented here, we train a neural network in an unsupervised manner to solve seismic interpolation problems using the local plane wave differential equation and the local slope estimated by the mean of plane wave destruction filters (PWD). The physics-informed neural network (PINN) maps the input grid points in time and space to the amplitudes of the wavefield whilst matching the information contained in the available traces. The proposed approach is tested on two seismic interpolation tasks using synthetic data, specifically, interpolation of data with large gaps and those aliased. Whilst the network shows remarkable interpolation capabilities in both experiments, it tends to struggle fitting aliased data with high frequency content. To mitigate this problem, we propose to include locally adaptive activation functions in the architecture. This leads to improved convergence and reconstruction accuracy.
  • rde-3 reduces piRNA-mediated silencing and abolishes inherited silencing in C. elegans.

    Priyadarshini, Monika; Al-Harbi, Sarah; Frøkjær-Jensen, Christian (Cold Spring Harbor Laboratory, 2022-08-15) [Preprint]
    Small RNA-mediated silencing of target genes can persist across generations and C. elegans is a well-established model for studying the molecular basis for epigenetic inheritance. We recently developed a piRNA-based inherited silencing assay that causes a high incidence of males by targeting him-5 and him-8. Acute gene silencing is determined in the presence of the piRNAi extra-chromosomal array and inherited silencing after loss of the piRNA trigger. This assay has the advantage of targeting endogenous genes that are easily scored in mutant backgrounds and obviates the need for mutant validation and genetic crosses, which can influence inherited silencing. Here we show an example of the assay by testing acute and inherited piRNA-mediated him-5 silencing in ribonucleotidyltransferase rde-3 (ne3370) mutant animals. In the absence of rde-3, acute silencing was reduced but still detectable, whereas inherited silencing was abolished.
  • Traveltime tomography and efficient physics-informed Bayesian inversion

    Qiao, Tian; Turkiyah, George; Schuster, Gerard T. (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Bayesian inversion provides the same information as regularized inversion of seismic data, except it also supplies a probability estimate of the solution throughout model space. The cost, however, is that Bayesian inversion is orders-of-magnitude more expensive than regularized inversion by a gradient optimization method. To mitigate this cost, we present an efficient physics-informed Bayesian inversion method that combines regularized inversion to get both the optimal solution and the posterior probability functions in model space. A gradient optimization method is used to efficiently estimate the maximum a posterior (MAP) solution, and so function evaluations are only needed around the MAP point in model space. This efficiently provides the posterior probability in that neighbourhood, and therefore avoids the tremendous expense of sampling points throughout the high-dimensional model space. We apply this physics-informed Bayesian inversion to VSP traveltime data. The tomogram is computed with the assistance of an analytic inverse, and the posterior probability estimate is computed with an order-of-magnitude less cost than standard Bayesian analysis. This procedure can also be adapted to refraction traveltime tomography for near-surface imaging.
  • Robust joint inversion and segmentation of 4D seismic data

    Romero, Juan; Ravasi, Matteo; Luiken, Nick (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Seismic inversion is the leading method to map and quantify changes from time-lapse (4D) seismic datasets, with applications ranging from monitoring of hydrocarbon-producing fields to carbon capture and sequestration. Time-lapse seismic inversion is however, a notoriously ill-posed inverse problem: the band-limited nature of seismic data, alongside inaccuracies in the repeatability of consecutive acquisition surveys make it challenging to obtain high-resolution, clean estimates of 4D effects. Adding prior information to the inversion process in the form of properly crafted regularization (or preconditioning) is therefore essential to successfully extract weak signals that are usually buried under strong noise. In this work, we leverage the fact that 4D seismic inversion can be described as a coupled inversion of its baseline and monitor 3D seismic datasets. In existing approaches, the coupling is introduced by penalizing the squared L2-norm of difference between the baseline and the monitor acoustic impedances, as this is usually assumed to be small. A major downside of such a regularization is that, whilst reducing the overall level of noise in the estimated acoustic impedance differences, the resulting 4D effects are usually oversmoothed and their strength is underestimated. We instead propose to adapt the joint inversion and segmentation algorithm introduced by Ravasi and Birnie (2021) to the problem of 4D seismic inversion. Our technique produces two acoustic impedance models by inverting the corresponding 3D seismic datasets, regularized by Total-Variation. Moreover, the objective function to optimize is augmented with a segmentation term that renders solutions consistent with the expected 4D effects (obtained, for example, as part of a 4D feasibility study by means of Gassmann fluid substitution). A numerical experiment is presented to validate the effectiveness of the proposed approach and its superiority over state-of-the-art 4D inversion methods.
  • Boosting self-supervised blind-spot networks via transfer learning

    Birnie, Claire Emma; Alkhalifah, Tariq Ali (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Self-supervised procedures offer an appealing alternative to supervised denoising techniques that require noisy-clean pairs of training data. However, the capabilities of self-supervised denoising procedures are often limited by the requirement that noise cannot be predicted directly from neighbouring values in the training input samples. As such, there is often a trade-off with respect to the number of training epochs between learning to replicate the signal without learning to replicate the noise. Focusing on blind-spot networks that learn a pixel’s value based on neighbouring pixels, we propose to train a supervised model in a blind-spot manner such that the model learns how to predict a pixel’s clean value based off its noisy neighbouring traces. The weights of the trained model are then used to initialise a self-supervised model which is trained purely on noisy field data. In comparison to the fully self-supervised approach, we illustrate that pre-training with synthetic data results in increased noise suppression, alongside a lower level of signal leakage in the field data.
  • Deep Earth: Leveraging neural networks for seismic exploration objectives

    Alkhalifah, Tariq Ali; Birnie, Claire Emma; Harsuko, Randy; Wang, Hanchen; Ovcharenko, Oleg (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Machine learning has already made many inroads in developments related to acquisition, processing, imaging, inverting, and interpreting seismic data. In spite of the many success stories, its commercial use has been limited as the challenges mount. These challenges include cost of training, availability of training samples, the applicability of the trained model to real data (generalization), and more importantly, the availability of practitioners who actually know what the neural networks (NNs) are doing. Taking a step back, I will review what worked in deep learning and what we are still waiting on to work. We will look into the various ML algorithms, from supervised to unsupervised, transformers to contrastive learning, and identify the potential role of these various algorithms on seismic data, with examples. The examples include seismic data denoising, data extrapolation, first arrival picking, microseismic location, velocity inversion all on real data.
  • Large-scale Marchenko imaging with distance-aware matrix reordering, tile low-rank compression, and mixed-precision computations

    Ravasi, Matteo; Hong, Yuxi; Ltaief, Hatem; Keyes, David E.; Vargas, David (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    A variety of wave-equation-based seismic processing algorithms rely on the repeated application of the Multi- Dimensional Convolution (MDC) operator. For large-scale 3D seismic surveys, this comes with severe computational challenges due to the sheer size of high-density, full-azimuth seismic datasets required by such algorithms. We present a three-fold solution that greatly alleviates the memory footprint and computational cost of 3D MDC by leveraging a combination of i) distance-aware matrix reordering, ii) Tile Low-Rank (TLR) matrix compression, and iii) computations in mixed floating-point precision. By applying our strategy to a 3D synthetic dataset, we show that the size of kernel matrices used in the Marchenko redatuming and Multi-Dimensional Deconvolution equations can be reduced by a factor of 34 and 6, respectively. We also introduce a TLR Matrix-Vector Multiplication (TLR-MVM) algorithm that, as a direct consequence of such compression capabilities, is consistently faster than its dense counterpart by a factor of 4.8 to 36.1 (depending on the selected hardware). As a result, the associated inverse problems can be solved at a fraction of cost in comparison to state-of- the-art implementations that require a pass through the entire data at each MDC operation. This is achieved with minimal impact on the quality of the processing outcome.
  • Physics-based preconditioned multidimensional deconvolution in the time domain

    Vargas, David; Vasconcelos, Ivan; Ravasi, Matteo; Luiken, Nick (Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022-08-15) [Conference Paper]
    Multi-Dimensional Deconvolution is a data-driven method that is at the center of key seismic processing applications - from suppressing multiples to inversion-based imaging. When posed in an interferometric context, it can grant access to overburden-free seismic virtual surveys at a given datum in the subsurface. As such, it constitutes an essential processing operation that achieves multiple imaging objectives simultaneously in redatuming or target-oriented imaging: e.g., suppressing multiples, removing complex overburden effects, and retrieving amplitude consistent image gathers for impedance inversion. Despite its potential, the deconvolution process relies on the solution of an ill-conditioned linear inverse problem sensitive to noise artifacts due to incomplete acquisition, limited sources, and band-limited data. Typically, this inversion is performed in the Fourier domain where the estimation of optimal regularization parameters hinders accurate waveform reconstruction. We reformulate the problem in the time domain - long believed to be computationally intractable - and introduce several physical constraints that naturally drive the inversion towards a reduced set of reliable, stable solutions. This allows to successfully reconstruct the overburden-free reflection response beneath a complex salt body from noise-contaminated data.
  • Elucidating biofouling over thermal and spatial gradients in seawater membrane distillation in hot climatic conditions

    Elcik, Harun; Alpatova, Alla; Gonzalez-Gil, Graciela; Blankert, Bastiaan; Farhat, Nadia; Amin, Najat A.; Vrouwenvelder, Johannes S.; Ghaffour, NorEddine (Water Research, Elsevier BV, 2022-08-14) [Article]
    Biofouling is a hurdle of seawater desalination that increases water costs and energy consumption. In membrane distillation (MD), biofouling development is complicated due to the temperature effect that adversely affects microbial growth. Given the high relevance of MD to regions with abundant warm seawater, it is essential to explore the biofouling propensity of microbial communities with higher tolerance to elevated temperature conditions. This study presents a comprehensive analysis of the spatial and temporal biofilm distribution and associated membrane fouling during direct contact MD (DCMD) of the Red Sea water. We found that structure and composition of the biofilm layer played a significant role in the extent of permeate flux decline, and biofilms that built up at 45°C had lower bacterial concentration but higher extracellular polymeric substances (EPS) content as compared to biofilms that formed at 55 °C and 65°C. Pore wetting and bacterial passage to the permeate side were initially observed but slowed down as operating time increased. Intact cells in biofilms dominated over the damaged cells at any tested condition emphasizing the high adaptivity of the Red Sea microbial communities to elevated feed temperatures. A comparison of microbial abundance revealed a difference in bacterial distribution between the feed and biofilm samples. A shift in the biofilm microbial community and colonization of the membrane surface with thermophilic bacteria with the feed temperature increase was observed. The results of this study improve our understanding of biofouling propensity in MD that utilizes temperature-resilient feed waters.
  • Stability analysis of the water bridge in organic shale nanopores: A molecular dynamic study

    Liu, Jie; Zhang, Tao; Sun, Shuyu (Capillarity, Yandy Scientific Press, 2022-08-13) [Article]
    In the last decades, shale gas development has relieved the global energy crisis and slowed global warming problems. The water bridge plays an important role in the process of shale gas diffusion, but the stability of the water bridge in the shale nanochannel has not been revealed. In this work, the molecular dynamics method is applied to study the interaction between shale gas and water bridge, and the stability can be tested accordingly. CO2 can diffuse into the liquid H2O phase, but CH4 only diffuses at the boundary of the H2O phase. Due to the polarity of H2O molecules, the water bridge presents the wetting condition according to model snapshots and one-dimensional analyses, but the main body of the water bridge in the two-dimensional contour shows the non-wetting condition, which is reasonable. Due to the effect of the molecular polarity, CO2 prefers to diffuse into kerogen matrixes and the bulk phase of water bridge. In the bulk of the water bridge, where the interaction is weaker, CO2 has a lower energy state, implies that it has a good solubility in the liquid H2O phase. Higher temperature does not facilitate the diffusion of CO2 molecules, and higher pressure brings more CO2 molecules and enhances the solubility of CO2 in the H2O phase, in addition, a larger ratio of CO2 increases its content, which does the same effects with higher pressures. The stability of the water bridge is disturbed by diffused CO2 , and its waist is the weakest position by the potential energy distribution.
  • High-Temperature Annealing Effects on Atomically Thin Tungsten Diselenide Field-Effect Transistor

    Khan, Muhammad Atif; Mehmood, Muhammad Qasim; Massoud, Yehia Mahmoud (Applied Sciences, MDPI AG, 2022-08-13) [Article]
    Two-dimensional (2D) material-based devices are expected to operate under high temperatures induced by Joule heating and environmental conditions when integrated into compact integrated circuits for practical applications. However, the behavior of these materials at high operating temperatures is obscure as most studies emphasize only room temperature or low-temperature operation. Here, the high-temperature electrical response of the tungsten diselenide (WSe2) field-effect transistor was studied. It is revealed that 350 K is the optimal annealing temperature for the WSe2 transistor, and annealing at this temperature improves on-current, field-effect mobility and on/off ratio around three times. Annealing beyond this temperature (360 K to 670 K) adversely affects the device performance attributed to the partial oxidation of WSe2 at higher temperatures. An increase in hysteresis also confirms the formation of new traps as the device is annealed beyond 350 K. These findings explicate the thermal stability of WSe2 and can help design 2D materials-based durable devices for high-temperature practical applications.
  • Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures

    Troidl, Jakob; Cali, Corrado; Gröller, Eduard; Pfister, Hanspeter; Hadwiger, Markus; Beyer, Johanna (Computer Graphics Forum, Wiley, 2022-08-12) [Article]
    High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
  • The first case of artemisinin treatment failure of plasmodium falciparum imported to Oman from Tanzania

    Subudhi, Amit; Bienvenu, Anne-Lise; Bonnot, Guillaume; Abu-Shamma, Reem; Khamis, Faryal; Lawati, Hussain Ali Abdulhussain Al; Picot, Stephane; Petersen, Eskild; Pain, Arnab (Journal of Travel Medicine, Oxford University Press (OUP), 2022-08-12) [Article]
    We present the clinical and genomic epidemiological perspective of the first case of Artesunate treatment failure in an Omani citizen admitted to a hospital in Muscat who originally contracted P. falciparum malaria during travel to Dar Es Salaam, Tanzania.
  • Life on the Urbach Edge

    Ugur, Esma; Ledinský, Martin; Allen, Thomas; Holovský, Jakub; Vlk, Aleš; De Wolf, Stefaan (The Journal of Physical Chemistry Letters, American Chemical Society (ACS), 2022-08-12) [Article]
    The Urbach energy is an expression of the static and dynamic disorder in a semiconductor and is directly accessible via optical characterization techniques. The strength of this metric is that it elegantly captures the optoelectronic performance potential of a semiconductor in a single number. For solar cells, the Urbach energy is found to be predictive of a material's minimal open-circuit-voltage deficit. Performance calculations considering the Urbach energy give more realistic power conversion efficiency limits than from classical Shockley-Queisser considerations. The Urbach energy is often also found to correlate well with the Stokes shift and (inversely) with the carrier mobility of a semiconductor. Here, we discuss key features, underlying physics, measurement techniques, and implications for device fabrication, underlining the utility of this metric.

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