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Independently evolved extracellular electron transfer pathways in ecologically diverse Desulfobacterota
No full textExtracellular electron transfer plays a role in the biogeochemical cycling of carbon, metals, sulfur, and nitrogen, and has wide-ranging biotechnological applications. The metal-reducing (Mtr), outer-membrane cytochrome (Omc), and porin-cytochrome (Pcc) pathways facilitate electron transfer to insoluble electron acceptors via trans-outer membrane cytochrome complexes. Although these pathways perform a similar function, they are phylogenetically unrelated, indicating independent evolutionary origins. Here, we report an extracellular electron transfer mechanism in which the high-current producing bacterium Desulfuromonas acetexigens differentially co-expresses, at transcript and protein levels, the porin-cytochrome, outer-membrane cytochrome, and metal-reducing pathways, along with high-molecular-weight cytochromes containing a large number of hemes (up to 86 heme-binding motifs), under extracellular electron transfer growth conditions (i.e., electrode under set potential or naturally occurring iron oxide minerals as the electron acceptor). Additionally, we identified over 40 Desulfobacterota species from diverse ecological environments that encode the outer-membrane cytochrome and metal-reducing pathways, with the majority also expressing the porin-cytochrome pathway. The newly identified metal-reducing proteins in Desulfobacterota form a major lineage, greatly expanding the known diversity of these proteins. To our knowledge, mtrCAB genes have not been reported in the Desulfobacterota phylum (formerly classified as Deltaproteobacteria), nor has any electroactive organism been shown to express these phylogenetically distant pathways simultaneously. These findings have ecological implications, challenging the belief that certain extracellular electron transfer pathways are exclusive to specific taxa, and suggesting that these pathways are more widespread than previously thought. Additionally, this reveals a previously unrecognized versatility in microbial electron transfer mechanisms that can be exploited in biotechnological applications.This work was supported by Center Competitive Funding Program (FCC/1/1971-33-01) to P.E.S. from King Abdullah University of Science and Technology (KAUST)
A Review of Data-Driven Machine Learning Applications in Reservoir Petrophysics
No full textReservoir petrophysical characterization stands as an essential initial step in petroleum production and gas storage operations. It involves the use of scientific and engineering tools to understand and explore the nature of the reservoir formation, its fluid content, and the most effective and efficient way of producing it. This involves determining the wetting behavior (wettability), the pore storage capacity (porosity), the quantity of individual fluids (saturation), and the ability of the reservoir to deliver its fluid to the wellbore (permeability). Conventional methods for determining these petrophysical properties such as the special core analysis laboratory (SCAL) and geophysical/petrophysical logs are being practiced. However, traditional SCAL, seismic, and logging methods are time-consuming and costly. Machine learning techniques are faster and help in analysis and better understanding of SCAL and logging methods, and it also provides reliable estimations of reservoir petrophysical properties. Therefore, this review provides a comprehensive overview of recent advancements in machine learning (ML) applied to reservoir petrophysics, covering applications in hydrocarbon exploration, enhanced recovery, and carbon dioxide (CO2) and hydrogen (H2) storage. Techniques for reservoir petrophysical characterization are explored, focusing on ML applications in rock typing, porosity/permeability estimation, fluid identification, and wettability assessment. Challenges and limitations associated with ML algorithms in petrophysical analyses are discussed, with insights into future research directions. The review encompasses a broad range of ML algorithms such as artificial neural networks, support vector machines, decision trees, and ensemble methods. Structured sections discuss ML-based petrophysical characterization, ML in CO2/H2 storage, integrated workflows combining ML with traditional methods, and challenges of ML applications in petrophysics. The review aims to illuminate the transformative impact of ML on reservoir petrophysics and its potential in CO2 and H2 storage, offering valuable insights for researchers and industry professionals. Promising results have been achieved with ML in predicting petrophysical properties, lithology classification, and fluid identification. Opportunities for further research and development in ML applications for reservoir petrophysics are identified, emphasizing the integration of ML with physics-informed models and conventional analysis methods. This review uniquely covers both laboratory and field data, making it a comprehensive resource for understanding ML techniques in reservoir petrophysics, spanning oil and gas reservoirs as well as CO2 and H2 subsurface storage operations.The authors received no financial support for the research
Shining Light on Hydrogen: Solar-Powered Catalysis with Transition Metals
No full textArtificial photosynthesis offers a promising pathway to address environmental challenges and the global energy crisis by converting solar energy into storable chemical fuels such as hydrogen. Among various photocatalysts, transition metal-based materials have garnered significant attention due to their tunable crystal phase, morphology, surface active sites, and other key properties. This review provides a comprehensive overview of recent advances in transition metal-based photocatalysts for hydrogen production, with a particular focus on modification strategies and their underlying mechanisms. By systematically classifying these materials, this work highlights effective approaches for enhancing their catalytic performance, including structural engineering, electronic modulation, and interfacial optimization. Furthermore, this work discusses the fundamental principles governing these modifications, offering deeper insights into their roles in charge separation, surface reactions, and stability. Finally, this work outlines future research directions and key challenges in the rational design of highly efficient transition metal-based photocatalysts for sustainable hydrogen production.C.F. and F.R. contributed equally to this work. This work received financial support from King Abdullah University of Science and Technology (KAUST) and Center of Excellence for Renewable Energy and Storage Technologies under award number 5937
Particle-Associated Bacterioplankton Communities Across the Red Sea.
No full textPelagic particle-associated bacterioplankton play crucial roles in marine ecosystems, influencing biogeochemical cycling and ecosystem functioning. However, their diversity, composition, and dynamics remain poorly understood, particularly in unique environments such as the Red Sea. In this study, we employed eDNA metabarcoding to comprehensively characterise bacterioplankton communities associated with pelagic particles in a three-dimensional assessment spanning depths from the surface to a depth of 2300 m along the full length of the eastern Red Sea within the exclusive economic zone of the Kingdom of Saudi Arabia. Our results reveal a diverse assemblage of taxa, with Pseudomonadota, Cyanobacteriota, and Planctomycetota being the dominant phyla. We identified pronounced spatial variability in community composition among five major Red Sea geographical regions, with a third of all amplicon sequence variants being unique to the Southern Red Sea in contrast to a relatively homogenous distribution along the water column depth gradient. Our findings contribute to a deeper understanding of microbial ecology in the Red Sea and provide valuable insights into the factors governing pelagic particle-associated bacterioplankton communities in this basin.This work was supported by National Center of Wildlife and King Abdullah University of Science and Technology
Integrated accuracy enhancement for the Fabry–Pérot interferometer: The multi-parameter approach series (MPAS)
No full textFabry–Pérot white light interferometry is at the heart of the surface force apparatus (SFA). One of the applications of SFA is the measurement of properties of sub-nanometer-confined fluids. For the determination of the properties of a confined fluid, the absolute accuracy of SFA is directly linked to the accuracy of the parameters describing the optical layers of the interferometer, particularly the micrometer thick mica spacer layers. During the measurement of nano-confined films, most of the optical path occurs within these mica spacer layers, which are thus identified as the major accuracy-limiting factor. This work describes an integrated accuracy enhancement method, the so-called multi-parameter approach series (MPAS), which is especially designed to determine the mica thickness and its dispersive refractive index in situ at enhanced accuracy, without the use of the conventional mica–mica contact calibration. We show how the proposed procedure increases the absolute instrumental accuracy by increasing spectral correlation. The proposed MPAS algorithm provides a significant accuracy enhancement and concurrently reveals the need to assess the elasto- and opto-mechanical properties of mica for any further accuracy improvements.We acknowledge financial support by the Swiss National Science Foundation (Grant No. 200021_204111). The idea that mica might exhibit significant opto-elastic effects in the SFA was brought up by J. Israelachvili in a casual discussion with M.H. more than three decades ago
A VOx based optoelectronic memristor for application in visual perception
No full textWhile biological vision systems excel at in-memory processing with low power consumption, traditional silicon-based vision chips struggle with high energy demands. This gap motivates the exploration of alternative materials for artificial intelligence applications. This paper presents a VOx-based optoelectronic synaptic memristive device. The proposed artificial synaptic device ITO/VOx /Pt mimics biological functions such as potentiation (P), depression (D), long-term memory (LTM), short-term memory (STM), and paired-pulse facilitation (PPF). The PPF index, standing at 105%, suggests a favorable pattern in short-term memory function. The device served as synapses within a spiking neural network (SNN) showing an achievable pattern classification accuracy of 88.68%, highlighting the potential of the VOx synaptic device for pattern classification tasks. The proposed VOx-based synaptic devices could be a promising platform for efficient pattern recognition and visual perception applications
Space-Based Earth Observations on Hotspots of Atmospheric NO2 over India Using Google Earth Engine: An Open-Source Cloud Platform
No full textThe atmospheric nitrogen dioxides (NO2) play an important role in tropospheric chemistry and climate change. The present study focused on utilizing the space based atmospheric NO2 observations and investigated variability in hotspots of NO2 concentration using Google Earth Engine over Indian regionThe predominant variability in NO2concentrations (~ 2-2.3 × 1016 molecules/cm2)is observed over Indo Gangetic Plain region(IGP). Over the Indian region forest fire hotspots were identified using MODIS/FIRMS data and highest number of forest fires was observed during the premonsoon. Lightning FlashRate (LFR)/ Lightning Flash Density (LFD) was estimated using Lightning Imaging Sensor (LIS) data on-board on Tropical Rainfall Measuring Mission (TRMM). Highest LFR is observed during monsoon season with values levelling between 120 to 180 Flashes yr− 1km− 2 while the lowest is observed during the post monsoon season with values ranging between 40 and 100 Flashes yr− 1km− 2during the years 2012–2014. Results indicated that Lightning Flash Rate is spatially correlated well with the hotspots of tropospheric NO2 concentration with correlation coefficients varying between 0.5 and 0.9 during monsoon.The present study also investigated the relationship between the lightning density, tropospheric NO2 concentration over the persistent NO2 hotspot regions. Further, a case study was examined over the Punjab and Haryana regions during 2019–2021 post-monsoon period for the active stubble burning in accordance with the severity classes and the burnt area was estimated. Moderate and High Severity is seen in different region of Punjab for almost all the years considered for analysis (during October-November).Authors acknowledge the Land Ocean and Atmospheric Greenhouse Gases Interaction Experiment (LOAGIN-X) and the Atmospheric Trace Gases \u2013 Chemistry, Transport, and Modelling (AT-CTM) Projects of ISRO-Geosphere Biosphere Program (ISRO-GBP) for supporting this work.There is no funding source for this work
Slowing Down in an Accelerated World: Understanding Degradation Pathways in Organic and Perovskite Photovoltaics for Extended Lifetime
No full textOrganic and perovskite solar cells face complex degradation mechanisms driven by environmental stressors, intrinsic material instabilities, and interfacial failures. In this talk, I will dissect the fundamental degradation pathways that limit operational lifetimes, from phase segregation and ion migration in perovskites to photochemical instability and morphological evolution in organics. By methodically studying these processes, we can develop strategies that slow down aging, extend lifetime without compromising efficiency. Through our latest research, I will highlight how targeted stabilization strategies, including compositional engineering and interfacial modifications, are assisting for durable, high-performance organic and perovskite solar cells.
This presentation will argue that ‘slow science’—carefully designed studies, FAIR data reporting, mechanistic insights and long-term studies—are critical in an era of accelerated technological development
Intergenerational metabolomic signatures of bleaching resistance in corals.
No full textCoral bleaching is one of the greatest threats to the persistence of tropical reef ecosystems. This necessitates identification of attributes associated with coral resistance and resilience to thermal stress, both within and between generations. Here, we use metabolomics to investigate the intergenerational biochemical signatures associated with heat-induced bleaching of Montipora capitata (the rice coral). By selectively breeding bleaching resistant or susceptible parents, we find metabolomic signatures of parental bleaching phenotype in sperm, eggs, embryos, larvae, and subsequent juvenile corals. Metabolome source mapping shows that these thermal tolerance signatures are from both coral host and algal symbiont, spanning a variety of molecular families. One of the strongest markers of intergenerational heat tolerance is the saturation state of DGCC betaine lipids, a molecular family previously associated with thermal tolerance in dinoflagellate symbionts of corals. Though DGCC lipid saturation state is strongly linked to algal genotypes, even coral progeny containing the more thermally susceptible Cladocopium algae show increased saturation of this lipid group if their parents had resisted recent bleaching events. This work provides evidence for biochemical inheritance as a potential mechanism for intergenerational acclimatization to warming oceans, which has substantial implications for reef conservation and restoration in the face of climate change.The authors would like to acknowledge funding from the National Science Foundation’s Organismal Response to Climate Change program under grant award number 2307516 to PI Quinn and Michigan State University’s internal Climate Change Research Grant
Data-driven planning of mixed-generation power systems: Towards 100% RES-based grids
No full textThe increasing penetration of renewable energy resources in modern power systems introduces significant stability, reliability, and operational challenges. As inverter-based resources (IBRs) replace conventional synchronous generation, maintaining stability margins becomes increasingly complex, requiring innovative planning and analysis approaches. This paper presents a data-driven framework that automates parameterization, simulation, and stability assessment for mixed-generation systems. Leveraging a ternary coordinate system, the approach enables intuitive visualization of stability trends as a function of the generation mix, supporting planners in identifying stability boundaries and optimal resource compositions. Automated eigenvalue analysis and root cause identification are combined with a Convolutional-Recurrent Neural Network (CRNN) that predicts stability directly from time-domain data, offering a scalable alternative to conventional model-based methods. Applied to modified 9-bus and 39-bus systems, and an HVDC-interconnected network, the framework demonstrates its effectiveness in capturing stability trends and defining acceptable generation mix regions, highlighting its value as a planning tool for the transition towards reliable and fully renewable grids.This work is part of a two-year project on developing intelligent solutions for grid technologies for ENOWA (NEOM) energy systems, funded by ENOWA (NEOM), Saudi Arabia, through a technical consultancy agreement with King Abdullah University of Science and Technology (KAUST). The research was also supported by the KAUST Center of Excellence for Renewable Energy and Storage Technologies (CREST) under award number 5937. The authors would like to thank the reviewers for their feedback, which helped to substantially improve the quality of this manuscript. A preliminary version of this work was presented at the IEEE Energy Conversion Congress and Exposition (ECCE) 2024. We also acknowledge the assistance of ChatGPT-4o [46] for language polishing. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us