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Numerical Simulation of Coupling Compositional Two-Phase Flow and Micro-Bio Reactive Transport in Underground Hydrogen Storage
No full textUnderground hydrogen storage (UHS) is a promising and scalable option for the storage of renewable energy. However, the inherent risks exist due to microbial activities, such as the potential loss of stored hydrogen (H2) and contamination by generated gases like methane (CH4) and hydrogen sulfide(H2S). To better understand the effects of subsurface microbial processes on UHS, it is essential to conduct numerical experiments to simulate the complex interplay between compositional two-phase fluid flow and bio-reactive transport. To account for compositional variations, we developed a flash calculation model based on a fugacity-fugacity framework designed for a hydrogen-brine system. In this framework, the Soave-Redlich-Kwong (SRK) Equation of State (EOS) was applied to calculate gaseous-phase fugacity, while Henry's law was used to estimate aqueous-phase fugacity. These EOS models were further rigorously calibrated against experimental data to ensure accuracy. This flash calculation framework was integrated into a general compositional simulator. Additionally, a double Monod model was incorporated to represent the kinetic growth and decay of microbial populations influenced by hydrogen injection. The compositional two-phase flow was ultimately coupled with bio-reactive transport, enabling the seamless integration of compositional changes driven by microbial metabolism activity within the simulator. Furthermore, the simulations revealed a distinct chemotaxis phenomenon, wherein bacteria migrated from the far field toward the hydrogen injection well during the UHS process. This study provides a comprehensive exploration of numerical simulations, integrating compositional two-phase flow with bio-reactive transport to develop an innovative simulator designed for UHS. The resulting framework serves as a robust tool for analyzing and managing the complex interplay between microbial activities and fluid dynamics in UHS, establishing a foundation for safer and more efficient UHS operations.The authors gratefully acknowledge funding support from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, through grants BAS/1/1423-01-01 and FCC/1/4491-37-01. We also extend our sincere gratitude to our colleagues from SINTEF, Norway, for their insightful discussions, particularly Dr. Knut-Andreas Lie, Dr. Olav Møyner, Dr. Elyes Ahmed, and Dr. Xavier Raynaud
V2Se2O and Janus V2SeTeO: Monolayer altermagnets for the thermoelectric recovery of low-temperature waste heat
No full textWe determine the thermoelectric properties of the V2Se2O and Janus V2SeTeO monolayer altermagnets with narrow direct band gaps of 0.74 and 0.26 eV, respectively. Monte Carlo simulations reveal Néel temperatures of 800 K for V2Se2O and 525 K for Janus V2SeTeO. The electrical conductivity is higher for p-type charge carriers than for n-type charge carriers due to lower effective masses. The presence of heavy Te atoms in Janus V2SeTeO results in lower phonon group velocities, higher phonon scattering rates, and higher lattice anharmonicity than in the case of V2Se2O, leading to an almost 19-fold reduction of the lattice thermal conductivity at 300 K. The thermoelectric figure of merit of V2Se2O reaches 0.4 (0.1) and that of Janus V2SeTeO reaches 2.7 (1.0) just below the Néel temperature at the optimal p-type (n-type) charge carrier density, demonstrating that altermagnets have excellent potential in the thermoelectric recovery of low-temperature waste heat.We thank Aamir Shafique for fruitful discussions. The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). For computer time, this research used resources provided by the KAUST Supercomputing Core Laboratory
Solution-processed tungsten diselenide as an inorganic hole transport material for moisture-stable perovskite solar cells in the n-i-p architecture
No full textSome of the obstacles to the commercialization of perovskite solar cells (PSCs) are their long-term moisture stability and material cost of the constituent layers, such as the commonly used spiro-OMeTAD hole transport layer (HTL). Replacing the spiro-OMeTAD with low-cost inorganic hole transport materials (HTMs) are important to further elevate the attractiveness of PSCs for commercialization. Perovskite-compatible, solution-exfoliated two-dimensional (2D) transition metal dichalcogenides (TMDCs) are being considered as viable candidates for inorganic HTMs. We consider one such TMDC, WSe2 which was chemically exfoliated using dichlorobenzene (DCB), a perovskite-compatible solvent, as it was integrated with triple cation perovskite absorbers within the solar cell stack. The WSe2 HTL required heat treatment processes to be maintained below 100 °C in order to preserve the integrity of the underlying perovskite; despite this lower temperature post treatment process, the structural morphology of the film revealed its dense and pinhole-free nature. Temperature-dependent transport studies conducted on the WSe2 film provided evidence of its semiconducting character and its ability to extract holes well from the underlying triple-cation Cs0.05FA0.79MA0.16PbI2.45Br0.55 absorber. The inorganic HTL offered better environmental stability in moisture-rich environments of up to 60 % relative humidity, in comparison to spiro-OMeTAD HTL-based devices which degraded faster as a result of pinholes.We thank the Office of Naval Research (grant number ONR N00014-20-1-2597) and the U.S. Department of Energy (grant number DE-NA0004114) who provided funding support that enabled us to pursue this work. Ehsan Ghavaminia is also acknowledged for his involvement in the initial effort, and assistance from Carlos Padilla, Tejal Pawale and Dr. Xiao Li from UNT, is also greatly appreciated. The cross-sectional SEM imaging was done in part using resources of the Shared Equipment Authority at Rice University. A.S.R.B. acknowledges support from King Abdullah University of Science and Technology (KAUST) through the Ibn Rushd Postdoctoral Fellowship Award
Fabrication and Characterization of Large Area Flexible Capacitive Pressure Sensors using PDMS Foam and Functionalized Textile
No full textFlexible capacitive pressure sensors have simple sensing structure, zero-temperature drift, linear response, and high sensitivity, leading to applications in human healthcare including soft and surgical robotics, human machine interaction (HMI) and wearable devices. In this paper, we present a highly compressible multilayer flexible capacitive pressure sensor, based on air/vapour bubble-induced PDMS foam thinfilm as dielectric and polypyrrole coated cotton textile as electrodes. An in-situ chemical oxidative polymerisation method was used to synthesize a conducting, large area polypyrrole coated cotton textile. We used this as parallel electrodes on the top and bottom of the bubble-induced PDMS foam. The bubble-induced PDMS film was made by casting of PDMS, mix with specific amounts of ethanol. The top and bottom textile electrodes were then attached to it with the help of silicone adhesive. We observed low hysteresis, high repeatability, and good step response in our sensor characterisation. Bubble characterisation was also performed including surface profilometer and scanning electron microscopy (SEM). We showcase two applications of our sensor including mouse click and grasping of a cup. The sensor can be fabricated in different shapes and sizes for various applications of free-form electronics such as soft and surgical robotics, wearable, biomedical, human-machine interaction, and so on.This work was supported in part by the Department of Science and Technology (DST), Government of India, through PURSE grant under Grant SR/PURSE/2022/119(G), and in part by the King Abdullah University of Science and Technology (KAUST) baseline fund
Challenges and Opportunities for Statistics in the Era of Data Science
No full textStatistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. While at the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions, nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory and computational aspects and often driven by other disciplines, such as computer science. However, statistics can—and should—contribute to the advances of data science. Of most interest are the strengths of statistics, such as the mathematical focus that leads to theoretical guarantees. This includes methods for formal modeling, hypothesis tests, uncertainty quantification and statistical inference. Of particular interest are also established statistical frameworks to handle causality or data deficiencies such as dependence, missingness, biases or confounding. This paper summarizes the findings of a discussion workshop on the topic that was held in June 2023 in Hannover, Germany. The discussion centered around the following questions: How must statistics be set up so that it can contribute (more) to modern data science? In which direction should it develop further? Which strengths can already be used now? What conditions must be created so that this can succeed? What can be done to arrive at a common language? What is the added value of formal modeling, inference, and the mathematical perspective taken in statistic
Scalable Nanogap Electrodes for Emerging Electronics and Sensor Applications
No full textModern telecommunication technologies, involved in the current 5G and upcoming the 6G telecommunication frequency ranges, rely on devices operating in the radio frequency spectrum of 0.3-100 GHz and 6-1000 GHz, respectively. Innovative fabrication methods and device designs are getting increasing attention with the goal of deploying them in new applications in order to meet the demanding performance requirements for the market. However, balancing fabrication complexity, manufacturing costs and performance presents formidable technical challenges for the industry. In this work there is a discussion about newly developed nanopatterning method called adhesion lithography to create coplanar zinc-oxide Schottky diodes and logic circuits. These Schottky diodes are easy to fabricate, providing high current rectification (≈105) with low reverse currents (≈80 pA) and a high cut-off frequencies of over 25 GHz. By integrating ZnO Schottky diodes on a wafer scale, logic circuits like logic gates and signal rectifiers were successfully implemented. The potential for more complex monolithic circuits was shown with the development of Half-Adder, Binary-Shifters and a Rectenna. This study presents an alternative manufacturing method for large-area radio frequency electronics and sets the foundation for diode-based circuit development for diode logic and rectifying applications.
Additionally, the human-machine interface hardware keeps becoming more common and more useful, which implies that better sensors are required to detect signals from various stimuli. Humidity sensors get attention for applications across various sectors, depending on moisture absorbing materials, which can take long recovery times, slowing their transient response and limiting their applications. This technical challenge is addressed by combining nanogap electrode architectures with albumen biopolymer as moisture-absorbing component. The resulting sensor is low-cost, highly responsive and selective to humidity, operating within a relative humidity range of 10-70% RH. The sensors show low/no-response to various interfering species, keeping high responsivity of >1.15×104 at room temperature. The nanometer range of nanogap structures enables fast temporal response, with rise and fall times of ~10 and ~28 ms, respectively, making the devices the fastest humidity sensors reported to date based on biomaterials. By leveraging these features, we demonstrate non-contact switching and real-time respiratory-cycle monitoring suitable for diagnosing chronic diseases
NUTCRACKER orchestrates cortical cell divisions and sustains stem cell niche integrity in the rice root meristem
No full textIn Arabidopsis thaliana, BIRD nuclear factors, also known as the INDETERMINATE DOMAIN (IDD) protein family, regulate asymmetric cell division and tissue patterning in the root meristem. The BIRD protein JACKDAW (JKD) forms a regulatory complex with the GRAS transcription factors SHORT-ROOT (SHR) and SCARECROW (SCR) to maintain the stem cell niche and stabilize tissue boundaries. While BIRD protein function is well characterized in Arabidopsis, their role in other plant species remains unclear.
Here, we show that in rice, the JKD ortholog OsNUTCRACKER (OsNUC) restricts cell division in the root meristem. Osnuc knockout mutants display ectopic divisions in the ground tissue and vasculature, a loss of quiescence in the quiescent center, and premature differentiation of columella stem cells. OsNUC associates with OsSHR and OsSCR and regulates the expression of OsSCR and QUIESCENT CENTER-SPECIFIC HOMEOBOX (QHB/WOX5). OsNUC is expressed in the ground tissue and exodermis and complements the Osnuc mutant when driven by its native promoter.
These findings reveal a role for the BIRD protein OsNUC in controlling cell division and maintaining the stem cell niche in the rice root meristem.We would like to thank Yu Wang, Qing Huan, Ke Li and Wenfeng Qian for providing the data plots for the BIRD gene expression from their rice single cell RNAseq (Wang et al., 2021). We would like to thank Trang Minh Dinh for her support in the lab and Xinjing Xu for the schematic representations of the WT and Osnuc root meristems. This research was supported by KAUST research baseline funds BAS/1/1081-01-01 and Competitive Research Grant (CRG 9) URF/1/4381-01-01 and CRG8 URF/1/4081-01-0
The Abundance Paradigm: Red Sea Algal Ecology and Economy in a Changing Climate
No full textMacroalgae structure coastal habitats and influence community composition. The Red Sea, a narrow and environmentally extreme basin with strong latitudinal gradients, provides a natural test for evaluating how macroalgal abundance can be constructive, competitive, or converted to societal value under climate change. This dissertation integrates (i) a systematic synthesis of Red Sea macroalgal research, which reveals pronounced spatial and thematic biases, including unresolved taxonomy, understudied calcifying taxa, and lack of foundational ecophysiology that limit climate-relevant inference; (ii) a coast-wide baseline of functional groups across coral reefs and seagrass meadows, showing strong latitudinal structuring and a nonlinear decline of coral cover with combined algal cover, including an empirical thresholds of reef collapse; (iii) stress experiment on an invasive algae peyssonnelid Ramicrusta sp. under low dissolved oxygen , in which respiration was progressively suppressed under nighttime deoxygenation whereas physiological processes maintained a coordinated tolerance with metabolic, metabolomic and microbial responses; (iv) a multi-species screen of macronutrients, elements, and targeted metabolomics of fatty acids, demonstrating phylogenetically structured portfolios and application-specific trade-offs that identify candidates for bioeconomy cultivation in arid coasts; and (v) untargeted metabolomic fingerprinting that resolves chemotaxonomic structure and confirms significant among-group differences, enabling species-level understanding for efficient bioprospecting potential. Collectively, the chapters show that macroalgal abundance is conditional, influenced by environmental variables, traits, and interactions, and that managing macroalgae abundance is pivotal for climate resilience. This work delivers decision-relevant thresholds for reef stewardship, mechanistic insight into a rising cryptic competitor, and data-driven shortlists that align a blue bioeconomy with ecological baselines in a changing sea
Accurate simulation of spontaneous Raman scattering of CO2 for high-temperature diagnostics
No full textThis paper presents a comprehensive simulation approach for the temperature-dependent Raman spectra of CO2, a common product in combustion and reactive environments. Previous studies have typically been limited to isotropic scattering or a restricted number of energy levels. In contrast, our simulation incorporates both isotropic and anisotropic scattering, including all ro-vibrational O, P, Q, R, and S transitions, and extends to all energy levels contained in and up to polyad 30, which our results demonstrate is essential for accurate modeling at high temperatures. The four most prevalent isotopologues [Formula presented], [Formula presented], [Formula presented], and [Formula presented] are included, collectively accounting for over 99.99 % of naturally occurring CO2. Polarizability ratios between the v1 and 2v2 modes and the isotropic/anisotropic contributions were determined by fitting them to experimental spectra at 296 K. The simulated CO2 spectra demonstrate excellent agreement with experimental data across temperatures up to 2355 K, thereby enhancing the reliability of Raman spectroscopy in various applications involving CO2.We gratefully acknowledge financial supports by the European Regional Development Fund (ERDF) under project number FPG991 0005/2019 and the project “Angewandte Forschung zur Wandlung wasserstoffbasierter Energieträger”. We also gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for the project ”Interference-resistant Raman spectrometer”, Germany (PN 514177753). G. Magnotti and D. Geyer acknowledge funding by King Abdullah University of Science and Technology (KAUST) under grant URF/1/3715–01–01
XGBoost meets INLA: a two-stage spatio-temporal forecasting of wildfires in Portugal
No full textWildfires pose a major threat to Portugal, with an average of over 115,000 hectares burned annually in the 45-year period of 1980-2024. Beyond a high number of ignitions, the country has experienced devastating mega-fires, such as those in 2017. Accurate forecasting of wildfire occurrence and burned areas is therefore essential for effective firefighting resource allocation and emergency preparedness. In this study, we present a novel two-stage ensemble approach that extends the widely used latent Gaussian modelling framework with the integrated nested Laplace approximation (INLA) for spatio-temporal wildfire forecasting. The first stage uses XGBoost, a gradient boosting model, to identify wildfire patterns from environmental covariates and historical fire records, producing one-month-ahead point forecasts for fire counts and burned area. These predictions are then incorporated as external covariates in a latent Gaussian model, which includes additional spatiotemporal random effects to produce the final probabilistic forecasts of monthly total fire counts and burned area at the council level. To effectively model both moderate and extreme wildfire events, we implement the extended generalised Pareto (eGP) likelihood (a sub-asymptotic distribution) within the INLA framework. We also develop and discuss penalised complexity priors (PC-priors) for the eGP parameters and provide a comprehensive comparison of the eGP likelihood against other commonly employed distributions in environmental modelling, such as the Gamma and Weibull distributions. The proposed framework addresses the challenge of accessing future environmental covariates, which are typically unavailable at prediction time, and demonstrates strong performance in one-month-ahead wildfire forecasting