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Data Science Meets Ziegler–Natta Catalysis to Design High-Performance Lewis Bases for Isotactic Polypropylene Production
No full textIn this study, we harness data science to design carbamate esters (CEs) as donors in Ziegler-Natta catalysis. Using a small yet insightful data set of 18 patented CEs, we developed a multivariate linear regression (MLR) model incorporating key electronic and steric descriptors to predict polymerization yields. Rigorous validation demonstrated the model’s robustness and predictive power, enabling its application in the discovery of higher performing CEs. In the initial optimization cycle, the model guided the design of 10 CEs, which were synthesized and tested, successfully confirming the predictions. A second optimization cycle fine-tuned the most promising CE from cycle 1, leading to the discovery of a highly efficient CE with a yield of 108 kg polymer/g catalyst, marking a 30% improvement over the best performing CE in our initial data set. This work underscores the transformative role of data science in industrial catalyst design, offering a powerful alternative to traditional trial-and-error and density functional theory approaches while accelerating innovation.L.C. thanks KAUST for financial support by grants URF/1/5577-01-01 and REI/1/5246-02-01. For computer time, this research used Shaheen III managed by the Supercomputing Core Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia
Fungi and Fungi-Like Entities
No full textCoral microbiology research has long focused on the composition and functional roles of prokaryotic organisms, but microeukaryotic communities, including the enigmatic fungi remain a poorly understood “black box” within coral and other holobionts. Here, we summarise what is known and hypothesised about the diversity, functional traits and potential, and chemodiversity of coral- and reef-associated fungi and fungi-like organisms (FLOs). Finally, we briefly outline the challenges associated with the characterization of marine fungi and provide a perspective for future studies to elucidate the biology, chemical ecology, and organismal interactions of marine fungi and FLOs within coral reef holobionts and their potentially far-reaching roles in coral reef ecosystem functioning and health
Survival strategies of Rhinocladiella similis in perchlorate-rich Mars like environments
No full textStudying the survival of terrestrial microorganisms under Martian conditions, particularly in the presence of perchlorates, provides crucial insights for astrobiology. This research investigates the resilience of the extremophile black fungus Rhinocladiella similis to magnesium perchlorate and UV-C radiation. Results show R. similis, known for its tolerance to acidic conditions, exhibits remarkable resistance to UV-C radiation combined with perchlorate, as well as to high concentrations of magnesium perchlorate, surpassing Exophiala sp. strain 15Lv1, a eukaryotic model organism for Mars-like conditions. Growth curve analyses revealed both strains can thrive in perchlorate concentrations mimicking Martian perchlorate-rich environments, with R. similis adapting better to higher concentrations. Morphological and protein production changes were investigated, and mass spectrometry identified perchlorate-induced proteins, advancing molecular understanding of potential microbial life on Mars. These findings advance knowledge of extremophile capabilities, contributing to the search for life beyond Earth and informing the design of future Martian rovers equipped for biosignature detection.This research was supported by KAUST Baseline Grant BAS/1/1096–01-01 (to Prof. A.S. Rosado) and the Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—Grant nos. 311152/2016-3 and 304867/2017-9, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Finance Code 001, and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)—Grant no. 2019/04900-2). A.S. is grateful to CAPES for his Ph.D. scholarship (Grant numbers 88887.598052/2021–00 and 88881.682425/2022–01)
Generative Adversarial Network for Modeling of CO2 Plume Evolution in Geological Carbon Storage Systems
No full textDeep learning-based surrogate models offer a powerful alternative to traditional numerical simulations for tackling subsurface multiphase flow challenges, such as those in Geological Carbon Storage (GCS). In this work, we utilized a deep learning surrogate model using Generative Adversarial Networks (GANs) to model CO2 saturation and pressure buildup evolution. GANs, with their generator-discriminator framework, excel at capturing intricate spatial and temporal patterns, enabling accurate predictions of CO2 plume saturation and pressure buildup in saline aquifers. By leveraging adversarial training, GANs effectively model structured data, preserving spatial relationships and generating high-resolution outputs. We first developed physics-based numerical simulation models to represent both the injection and post-injection phases of GCS. Using Latin-Hypercube sampling, we created a diverse set of reservoir and decision parameters, forming a comprehensive simulation database. The GANs were trained and evaluated on a 2D model to assess their performance. During training, we employed Mean Squared Error (MSE) and spatial derivative-based loss functions to optimize the model hyperparameters. The GANs demonstrated strong performance, achieved R2 values of 0.989 and 0.996 for saturation and pressure buildup predictions, respectively. The Normalized Absolute Error (NAE) remained consistently around 1% across all predictions, highlighting the model’s accuracy in capturing the temporal and spatial evolution of CO2 saturation. Additionally, the GANs demonstrated remarkable computational efficiency, with prediction times of just 0.01 seconds per case, compared to 1000 seconds for the 2D model using physics-based simulations. These results underscore the potential of GANs to deliver highly accurate predictions while significantly reducing computational costs.Authors would like to acknowledge King Abdullah University of Science and Technology and Saudi Aramco for funding this work under the grant RGC/3/6332-01-01
Effects of Hydrogenated and De-Hydrogenated Organic Hydrogen Carriers on Carbonate Wettability for Hydrogen Geological Storage
No full textOrganic hydrogen carriers (OHCs) have emerged as a promising solution for the efficient large-scale storage and transport of hydrogen, thus helping to address the increasing demands for renewable energy and decarbonization. The ability to store hydrogen geologically is influenced by the wetting properties and interfacial forces between the OHCs and subsurface formations, with significant impacts on the residual saturation, fluid flow dynamics, injection/withdrawal rates, and containment reliability. Herein, the advancing and receding contact angles and interfacial tension (IFT) of methylcyclohexane (MCH) and toluene are measured on calcite substrates in the presence of 1 M NaCl solution under natural physio-thermal geological conditions (298–343 K, 1–20 MPa). In addition, the MHC-exposed calcite samples are characterized via atomic force microscopy, X-ray diffraction, scanning electron microscopy, energy-dispersive spectroscopy, Fourier transform infrared spectroscopy, and total organic content analysis. The results suggest that the wettability and IFT values increase with increasing pressure and decrease with increasing temperature. This is attributed to increased intermolecular interactions between the liquid molecules and solid surface, along with the reduced density and surface energy of each liquid on the positively charged rock surface. However, due to the density difference between hydrogenated and dehydrogenated forms, MCH has a higher IFT and lower wettability than toluene at a given pressure and temperature. The findings demonstrate the viability of OHC integration into carbonate reservoirs for enhanced and secure hydrogen storage capability, and underscore the importance of optimizing OHC interactions with geological substrates to improve the hydrogen storage efficiency for advanced sustainable energy solutions.This publication is supported by the King Abdullah University of Science and Technology (KAUST) Research Funding Office under Award no. RGC-5635
Light limitation and water velocity modify the impacts of simulated marine heatwaves on juvenile giant kelp.
No full textCoastal regions are complex habitats, where multiple natural and anthropogenic drivers can interact to affect the survival and growth of marine organisms. The giant kelp Macrocystis pyrifera is sensitive to increasing seawater temperatures and susceptible to marine heatwaves. Light availability and hydrodynamics can also affect the growth, morphology, and resilience of this species. In this experiment, juvenile sporophytes of M. pyrifera from Scorching Bay, Wellington, Aotearoa, New Zealand, a were exposed to a combination of simulated marine heatwaves at one of four different temperatures (20, 22, and 24°C compared to a 16°C control), one of two irradiance levels (shaded: 0.9 mol photons · m-2 · d-1 or ambient: 1.4 mol photons · m-2 · d-1), and one of two flow speeds (5.3 cm · s-1 or 6.1 cm · s-1) in a fully factorial design. Simulated heatwaves lasted for 21 days, with temperatures ramped by 2°C · d-1, followed by a 21-day recovery phase. The heatwave treatments represented severe heatwaves in present day or hypothetical future conditions, whereas the control represented historical average summer sea temperatures in Wellington, and 21 days represented a realistic duration for heatwaves in this region. Temperature was the main driver of negative physiological impacts, with 100% of sporophytes dying within 42 days of exposure to a 24°C heatwave. Sporophytes experienced 44% mortality at 20°C and 81% mortality at 22°C, and growth rates declined significantly with increasing temperature. However, survival rates were modified by light and water velocity, with 56% of sporophytes surviving under a combination of ambient light and fast water velocity, compared with less than 50% under each of the other light-velocity combinations. Light limitation also reduced sporophyte survival, growth rates, and effective quantum yield. Water velocity alone did not significantly affect sporophytes, but flow speeds had interactive effects with temperature and light. The findings of this experiment suggest that M. pyrifera at sites with optimal environmental conditions, including low sediment loads and fast tidal flows, could be more resilient to marine heatwaves, as long as temperatures do not exceed critical thresholds for survival.This research was supported by funding from the Coastal People, Southern Skies Centre for Research Excellence project to CEC (E4280), a Rutherford Discovery Fellowship to CEC (VUW 1701), the Wellington Community Fund, the Eurofins Foundation, and the Clare Foundation
Direct numerical simulation of particle-laden turbulent channel flow over superhydrophobic surfaces
No full textWe investigate the effects of superhydrophobic surfaces (SHS) consisting of streamwise-aligned grooves on turbulence dynamics and drag reduction in a fully developed turbulent particle-laden channel flow. The SHS is modeled as a flat boundary with alternating no-slip and free-slip conditions, and a series of two-way coupled point-particle direct numerical simulations are conducted, systematically varying the SHS texture spacing and free-slip area fraction while selecting particle parameters that induce pronounced turbulence modulation and drag reduction. Our results show that particles enhance drag reduction compared to particle-free flows over the same SHS configurations. To elucidate the coupling mechanism between particles and SHS, we analyze turbulence, particle statistics, and secondary flow motions. Particles suppress near-wall vortical structures, reducing Reynolds shear stress and disrupting the phase symmetry of secondary flows. Simultaneously, particle dynamics are influenced by the modulated turbulence, leading to complex nonlinear interactions. The particle–turbulence interactions enhance drag reduction through two competing mechanisms: direct suppression of fluid turbulence and particle-induced stresses. A quantitative analysis of the friction drag coefficient using the drag decomposition reveals that the particle effect on laminar and slip contributions remains relatively weak across various SHS configurations. However, the reduction in fluid turbulence contribution consistently outweighs the particle-phase contribution, leading to an overall enhancement of drag reduction. This study provides insights into the synergistic effects of SHS and particles on drag modulation in turbulent flows.For computer time, this research used Shaheen III managed by the Supercomputing Core Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. This work was funded by King Abdullah University of Science & Technology (Grant Numbers: BAS/1/1663-01-01 and OSR-2019-CCF-3666)
Isolation, Fixation and Characterization of Juvenile Gilthead Seabream Head Kidney Leukocytes by Flow Cytometry.
No full textImmunity is crucial for the physiological regulation of organisms, serving as the primary defense against pathogens and environmental stressors. The isolation and analysis of immune cells provide key insights into immune responses to external pressures. However, the lack of harmonized protocols for less studied species, such as marine fish, often leads to technical and analytical challenges that hamper data interpretation and a thorough understanding of species-specific immune responses. This study aimed to set up an optimized flow cytometry-based analytical procedure to characterize and determine the viability of leukocytes from the head kidney (the main hematopoietic organ in teleost fish) of juvenile gilthead seabream (Sparus aurata). The procedure began with the isolation of leukocytes through a homogenization process using Hanks' balanced salt solution, followed by an optimized Percoll density gradient centrifugation method to ensure high recovery rates of leukocytes with minimal erythrocyte contamination required for efficient subsequent flow cytometry analysis. Additionally, a novel technique using a cell-reactive dye (LIVE/DEAD Fixable Dead Cell Stain Kit) was employed to distinguish viable from dead cells based on their fluorescent staining patterns. Fixation was achieved with 3.7% formaldehyde, preserving cell morphology, viability, and staining efficiency. Flow cytometry analysis successfully identified three predominant leukocyte populations: lymphocytes, monocytes, and granulocytes. This method not only allowed viability tests but also the accurate differentiation of cell types. The improvement in flow cytometry protocols represents a step forward in fish immunology by increasing the accuracy and efficiency of immune cell analysis. Furthermore, by allowing the fixation of cells for later analysis, this protocol significantly reduces the time and effort required for immune assessments, making it a valuable tool for both research and practical applications in various fields of research
Effects of Fuel Diluents on Flame Characteristics of Laminar Methane-Oxygen Inverse Diffusion Flames
No full textInverse co-flow diffusion flames (IDF) are the fundamental flame configuration in which autothermal reforming (ATR) of natural gas is based, a technology for clean hydrogen production. However, soot formation is unavoidable for IDFs because of fuel-rich conditions. This study assessed the effects of various diluents, including carbon dioxide (CO2), nitrogen (N2), argon (Ar), and helium (He), introduced into the fuel stream on the properties of oxy-fuel laminar IDFs, with the aim of improving the understanding of soot formation in IDFs at atmospheric pressure. The flame structure, temperature, syngas (H2+CO), polycyclic aromatic hydrocarbons (PAHs), and soot formation in methane IDFs were investigated using laser-based diagnostic techniques and numerical simulations. Pure oxygen (O2) was used as an oxidizer to mimic the ATR process. Results show that diluent addition reduces the peak flame temperature and shifts the flame structure axially downstream, increasing the flame height due to buoyancy-induced acceleration and slower diffusion. OH-PLIF measurements reveal that CO₂-diluted flames exhibit the longest flame lengths, linked to Peclet number (Pe) trends and suppressed buoyancy-driven radial convection. PAH formation follows the order: He > Ar > N2 > CO2, with CO2 reducing PAH levels by promoting oxidation of key intermediates via increased OH production. Soot spatial distribution is shifted downstream, with the peak soot volume fraction (SVF) following Ar > N2 > He > CO2, correlating with flame temperature and residence time. CO2 had the strongest soot suppression effect, acting through both thermal and chemical mechanisms. Numerical results indicate that temperature and OH mole fraction govern the syngas composition. CO2 dilution resulted in higher CO and lower H₂ production, as reaction pathway analysis showed that CO2 enhances OH and CO formation while reducing H radicals, limiting H₂ generation. These findings provide insights into the role of diluents in controlling soot and syngas formation in IDFs.The authors gratefully acknowledge the funding from the KAUST CRG project [URF/1/4688-01-01].COMBUSTION SCIENCE AND TECHNOLOGY 2
Handling Device Heterogeneity in Federated Learning: The First Optimal Parallel SGD in the Presence of Data, Compute and Communication Heterogeneity
No full textThe design of efficient parallel/distributed optimization methods and tight analysis of their theoretical properties are important research endeavors. While minimax complexities are known for sequential optimization methods, the theory of parallel optimization methods is surprisingly much less explored, especially in the presence of data, compute and communication heterogeneity.
In the first part of the talk [7], we establish the first optimal time complexities for parallel optimization methods (Rennala SGD and Malenia SGD) that have access to an unbiased stochastic gradient oracle with bounded variance, under the assumption that the workers compute stochastic gradients with different speeds, i.e., we assume compute heterogeneity. We prove lower bounds and develop optimal algorithms that attain them, both in the data homogeneous and heterogeneous regimes. In the second part of the talk [6], we establish the first optimal time complexities for parallel optimization methods (Shadowheart SGD) that have access to an unbiased stochastic gradient oracle with bounded variance, under the assumption that the workers compute stochastic gradients with different speeds, as before, but under the further assumption that the worker-to-server communication times are nonzero and heterogeneous. We prove lower bounds and develop optimal algorithms that attain them, in the data homogeneous regime only. Time permitting, I may briefly outline some further recent result