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Collapsing Carbon Nanotube Enhances Its Phonon Transport
No full textCarbon nanotubes (CNTs) radially deform when they interact with the surrounding matrix in heterostructures or metal electrodes in electronic devices, affecting their electrical properties. As thermal management becomes increasingly important for high-performance CNT-based nanoelectronics, understanding how such deformations affect the thermal conductivity (κ) of CNT-based devices has emerging significance. The investigation shows that the CNT's radially malleable nature enables the CNT to collapse, allowing atoms across the circumference to couple directly and enhance its thermal transport. Through solving the phonon Boltzmann transport equation at 300 K, the κ of a long (6,6) CNT increases up to six times upon radial compression to 18 GPa. The carbon–carbon bonds become stretched but the acoustic and optical phonons of non-longitudinal polarizations are surprisingly stiffened. This stiffening weakens the anharmonicity, leading to an increase in the phonon relaxation time and κ. However, for CNTs shorter than 103 nm, a peak in κ occurs with increasing stress. This peak is produced as the increased phonon-boundary scatterings in shorter CNTs offset the increased phonon relaxation time at high stress. Hence, an optimal stress level can increase the κ of CNTs, optimizing the performances of radially-deformed CNT heterostructures.This publication was based upon work supported by the National Natural Science Foundation of China (Grant: 52350610259), ZJU\u2010YST Joint Research Center for Fundamental Science, the Zhejiang\u2010Saudi Energy Materials International Collaboration Laboratory, and State Key Laboratory (SKL) of Biobased Transportation Fuel Technology
Dose-Dependent Effects of Myo-Inositol on Kainic Acid-Induced Epilepsy: Electrophysiological, Behavioral, Transcriptomic, and DNA Methylome Studies
No full textEpilepsy is a prevalent neurological disorder characterized by spontaneous recurrent seizures (SRS). Epileptogenesis is a multifaceted pathophysiological process that transforms a normal brain into one prone to chronic seizures. Targeting epileptogenesis is a compelling line of epilepsy therapy. Thus, discovering new drugs that oppose, mitigate, or modify epileptogenesis is a significant challenge in modern neuroscience. Our previous work demonstrated that, in a kainic acid (KA)-induced post-status epilepticus model, 28 days myo-inositol (MI) treatment reduces frequency and duration of motor and electrographic SRS even following cessation of treatment, for the following 4 weeks and identified MI as a promising antiepileptogenic compound To further evaluate the dose-dependent efficacy of MI, we applied the same experimental model using 30 mg/kg (dose used in earlier studies), 60 mg/kg, and 120 mg/kg to assess effects on hippocampal electrographic and motor SRS, as well as KA-induced spatial learning and memory impairment in a Morris water maze test. We found that MI had long-lasting, dose-dependent suppressive effects on behavioral and electrographic manifestations of epileptogenesis and ameliorated spatial learning and memory deficit induced by SE, with 60 mg/kg emerging as the most effective dose. Furthermore, we investigated transcriptomic and epigenetic alterations associated with the optimal MI dose and identified multiple affected pathways in the hippocampus. Interestingly, MI treatment resulted in transcriptomic upregulation and prevention of downregulation of several ion channel subunits, including GRIK3 and GRIN3A (kainate and NMDA receptor subunits) and the sodium channel subunit SCNB4. The obtained data highlight new molecular targets for epilepsy therapy and support the translational potential of MI.Thanks to Ilia State University (Tbilisi, Geporgia) for administrative support.
Supported by Shota Rustaveli National Science Foundation (Grant № FR-21-5004). Dr. Merab Kokaia was supported by the Swedish Research Council (Grant N 2021-03209)
T2Bs: Text-to-Character Blendshapes via Video Generation
No full textWe present T2Bs, a framework for generating high-quality, animatable character head morphable models from text by combining static text-to-3D generation with video diffusion. Text-to-3D models produce detailed static geometry but lack motion synthesis, while video diffusion models generate motion with temporal and multi-view geometric inconsistencies. T2Bs bridges this gap by leveraging deformable 3D Gaussian splatting to align static 3D assets with video outputs. By constraining motion with static geometry and employing a view-dependent deformation MLP, T2Bs (i) outperforms existing 4D generation methods in accuracy and expressiveness while reducing video artifacts and view inconsistencies, and (ii) reconstructs smooth, coherent, fully registered 3D geometries designed to scale for building morphable models with diverse, realistic facial motions. This enables synthesizing expressive, animatable character heads that surpass current 4D generation techniques
Ultrawide-bandwidth boron nitride photonic memristors
No full textPhotonic memristors based on two-dimensional materials are emerging as critical components for ultrascalable, energy-efficient artificial vision systems, integrating opto-sensing, data storage and processing capabilities. However, existing devices typically exhibit narrow spectral response ranges and operate in a single mode (for example, non-volatility), limiting their applications in complex computing scenarios. Here we introduce photonic memristor arrays based on a wafer-scale hexagonal boron nitride (hBN)/silicon (Si) heterostructure. These memristors are developed via in situ, low-temperature (250 °C), large-area growth of highly homogeneous hBN films on Si-based substrates. The devices exhibit opto-reconfigurability across a broad spectral range from ultraviolet to near infrared. By adjusting the incident laser power, the device can be reconfigured between non-resistive-switching, volatile and non-volatile modes. This light-induced reconfigurability is attributed to the formation of conductive filaments through interactions between hydrogen ions and photogenerated electrons within the engineered hBN/Si heterostructures. Furthermore, the photonic memristor features a switching ratio exceeding 109, retention time surpassing 40,000 s, endurance over 106 cycles and thermal stability up to 300 °C. These findings provide a scalable solution for developing integrated sensing-storage-computation artificial vision systems, fully compatible with sophisticated Si-based semiconductor technologies.This work is supported by the King Abdullah University of Science and Technology, Office of Sponsored Research (OSR), under award numbers ORA-CRG10-2021-4665 and ORA-CRG11-2022-5031 and the Semiconductor Initiative
From Language to Action: Enabling Robotic Manipulation Through Foundation Models and Scene Graphs
No full textThis thesis investigates the capabilities of off-the-shelf foundation models in robotic manipulation tasks without domain-specific training. Large Language Models such as GPT-4 and Gemini are employed alongside Vision-Language Models like QwenVL to enable a UR10e robotic arm to perform manipulation tasks through natural language instruction. The research introduces a novel approach where Large Language Models ``see'' the world by engaging in dialogues with Vision-Language Models, which can also provide spatial grounding through bounding boxes translated into coordinates via a custom algorithm. A scene graph aids the Large Language Model by grounding it in the workspace to produce logical task sequences, while motion planning utilises a custom implementation built on Nvidia's CUDA-powered Curobo planner. The methodology is evaluated through increasingly complex experiments including basic manipulation tasks, common benchmarks such as block stacking and Tower of Hanoi, and advanced manipulation like object classification and sorting that involves scene graph-based planning. The experiments demonstrate that foundation models can effectively interpret visual scenes, plan multi-step manipulation sequences, and leverage their inherent knowledge bases to accomplish tasks traditionally requiring specialised datasets and training. This research contributes by showing how pre-trained foundation models combined with robotic systems create flexible manipulation capabilities while reducing the need for task-specific training, potentially accelerating robot deployment in diverse real-world applications
Scalable Production of Highly-Reliable Graphene-Based Microchips.
No full textGraphene is a gapless 2D material that could be used to fabricate superior electronic devices and circuits, particularly useful in the fields of telecommunication and sensing. While promising performance has been demonstrated at the laboratory scale, graphene integrated circuits at the wafer level suffer from poor reliability due to native defects, especially at interfaces with dielectrics and electrodes. Here, we show the fabrication of highly reliable graphene-based microchips, containing transistors and frequency doublers, on 200 mm wafers through a multi-project wafer tape-out. Our transistors use multilayer hexagonal boron nitride (hBN) as gate dielectric, and they exhibit record performance in terms of reliability. In particular, our hBN/graphene transistors show ultra-low hysteresis below 20 mV and negligible shifts of the on-state current and the charge neutrality point even after 2100 cycles. The ultra-stable response of our hBN/graphene transistors contrasts with that of devices using metal-oxide gate dielectrics (HfO2, Al2O3), which exhibit severe degradation after a few dozens of cycles. These results, consistent across multiple devices, show low variability and demonstrate a scalable process for mass production of graphene-based microchips.This work has been supported by the generous Baseline fund and the Opportunity Fund Project 2023 under PID URF/1/5578\u201001\u201001 of the King Abdullah University of Science and Technology. AIXTRON acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 952792 (2D\u2010EPL) and from BMBF (Neurotec II (16ME0403) and NeuroSys (03ZU1106AD) projects). Prof. Mario Lanza acknowledges the platform Web Of Talents ( https://weboftalents.com ) for support on the recruitment of students and postdocs
Tropical-extratropical interactions: the atmospheric dynamics behind Dubai's extreme precipitation in April 2024
No full textExtreme precipitation events (EPEs) are becoming more frequent and intense under a warming climate, posing escalating risks to arid urban regions, emphasising the critical need to understand their atmospheric triggers. This study investigates the large-scale atmospheric dynamics behind an EPE that occurred in Dubai on April 16 2024, leading to catastrophic flooding and substantial economic losses. We employ a multifaceted approach to examine daily precipitation anomalies and large-scale atmospheric conditions preceding the EPE. Backward trajectories, simulated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, are analysed to trace the origin and transport of air masses contributing to the event. Spatial patterns and vertical cross-sectional analyses of atmospheric diagnostic variables are conducted to explain the EPE dynamics. The findings reveal that a combination of cold air anomalies aloft and warm air anomalies in the mid and lower troposphere, coupled with an intrusion of cold air from the north, likely driven by a subtropical jet (STJ)-induced trough, led to baroclinic instability and triggered strong convective activity, resulting in the EPE. Strong negative geopotential height anomalies across various pressure levels indicate a barotropic atmospheric structure. This configuration facilitated strong low-level convergence and upper-level divergence, promoting a chimney-like mechanism that enhanced moisture transport from surrounding water bodies—the Arabian Gulf, Arabian Sea, and Red Sea. Additionally, the progressive intensification of cyclonic vorticity further enhanced the deep convective circulation anomalies, contributing to the development of the EPE. These findings suggest that identifying specific atmospheric precursors may improve the predictability of the EPEs in Dubai.This research was funded by Zayed University, Abu Dhabi, UAE, under Research Incentive Fund No. 23021
Transient-Promoter-Stabilized NiFe Oxyhydroxide Enables Durable kW-Scale Water Splitting Under Fluctuating Power
No full textRenewable-powered water electrolysis provides a carbon-neutral route to hydrogen, yet large-scale deployment is constrained by reliance on stable but carbon-intensive grid electricity. Direct integration with fluctuating renewable power requires catalysts and devices that can endure dynamic operating conditions. Here we present a transient-promoter strategy for NiFe oxyhydroxide oxygen evolution reaction (OER) catalysts, realized from Ni3Fe1.2Cr0.8Ox precursor, for kilowatt-scale anion exchange membrane water electrolyzers (AEMWEs). Ex situ and operando spectroscopy establish that Cr (i) modulates Ni/Fe oxidation states to enrich positive charge and facilitate oxyhydroxide formation, (ii) induces porosity that enhances electrolyte penetration and OH− adsorption, and (iii) leaches sacrificially to protect Ni/Fe active sites. Lab-scale AEMWE device achieves an industrially relevant current density of 1 A cm−2 at a cell voltage of 1.68 V and sustains continuous operation for over 30 days under both constant and fluctuating loads. Scaling from 1 cm2 AEMWE to an 8-cell, 512 cm2 stack, the system can handle an electrical power of 2.5 kW at peak, and delivers 1 A cm−2 at 1.78 V per cell at 60 °C. The stack remains resilient over 13 simulated solar cycles (>50 h), underscoring the feasibility of integrating renewable electricity with durable, NiFe oxyhydroxide OER catalyst based AEMWEs.The authors acknowledge the financial support and research facilities from King Abdullah University of Science and Technology (Baseline Fund, BAS/1/1413-01-01). The authors also thank Shanghai Bright-H Technology Co. Ltd., and Saudi Aramco for collaborative support. The authors thank Thom Leach (scientific illustrator, KAUST) for helping with the Figures 1, 3, and 4c and TOC graphic
Pt<sub>3</sub>Co Alloy Nanoclusters as Charge Separation and Reduction Sites for the Enhanced Photoreduction of CO<sub>2</sub> on Mo<sub>2</sub>C MXene
No full textThe design and preparation of highly efficient and stable cocatalysts are critical for improving the photocatalytic CO2 reduction performance. A traditional cocatalyst consists of metal nanoparticles that facilitate the separation of photoinduced electron–hole pairs and the reduction of protons. In this research, the Pt3Co alloy nanocluster cocatalyst was loaded onto Mo2C MXene to enhance photocatalytic CO2 reduction activity and CO selectivity. As anticipated, the optimized Pt3Co/Mo2C-5 exhibited a 3.2-fold increase in CO2-to-CO conversion efficiency compared to individual Mo2C MXene, with selectivity rising from 63.94% to 81.75%. The photoelectrochemical experiments and in situ transmission FTIR results further validated that the Pt3Co/Mo2C catalyst possesses excellent charge separation efficiency, providing more reduction active sites for CO2 reduction reactions. This work offers novel insights into the utilization of alloy clusters and Mo2C MXene in photocatalytic CO2 reduction.This work was supported by the National Natural ScienceFoundation of China (22208127); The Senior Talent ResearchFoundation of Jiangsu University (22JDG017 and 23JDG030);and KAUST Baseline Fund BAS/1/1433-01-01
Toward a Data Driven Library Decision: Library Services Dashboard
No full textIn today’s digital world, most services are delivered online or in digital formats, generating vast amounts of data. This data holds immense value when effectively analyzed and transformed into actionable knowledge. Libraries, as service-oriented organizations, produce extensive data through traditional services such as acquisition, circulation, collections, and cataloging, as well as emerging areas like digital repositories, digital preservation, online reference services, and research data management. These data streams present unparalleled opportunities for libraries to shape strategies, optimize operations, and enhance services.
The KAUST Library has a well-established commitment to leveraging data for the continuous enhancement of its services and operations. This commitment was significantly reinforced by the university-wide launch of a comprehensive Lean Six Sigma program in 2011, an initiative designed to systematically improve process efficiency and service quality across the entire institution.
Building directly on this foundation of data-driven improvement, the Library strategically embarked on the development of its Library Services Dashboard in 2017. This innovative dashboard was conceived as a centralized, integrated tool specifically designed to document, rigorously monitor, and effectively control the vast array of library services. By providing a unified platform for data collection and analysis, the Dashboard empowered the Library to gain deeper insights into performance, identify areas for optimization, and ensure that its offerings continually meet the evolving needs of the KAUST community. This proactive embrace of Lean Six Sigma principles and analytical tools underscored the Library’s dedication to operational excellence and user-centric service delivery.
Building on its established foundation of data-driven improvement and the pre-existing Library Services Dashboard, the KAUST Library found its foresight particularly validated during the unprecedented COVID-19 pandemic. This global crisis underscored the critical importance of robust data infrastructure.
The dashboard, functioning as a central hub for library operations, enabled real-time monitoring of crucial resources and services. With physical access to the Library often restricted, the ability to instantly track trends in electronic resource usage, remote reference queries, and virtual program attendance became indispensable. This continuous, centralized stream of insights directly informed critical decision-making, allowing the Library leadership to swiftly pivot strategies. For instance, data from the dashboard revealed immediate shifts in demand for specific digital collections or online support, prompting rapid adjustments in licensing agreements, content acquisition priorities, and the deployment of remote assistance. This agility ensured the uninterrupted continuity of essential services to the KAUST community, minimizing disruption to teaching, learning, and research. The dashboard’s capacity to provide actionable data in times of crisis not only demonstrated its immense value but also solidified the Library’s reputation as a resilient and adaptable partner in supporting the university’s mission, even under extreme pressure.
In this paper, we will explore in depth:
• Identification of Service-Related Systems and Processes. We will detail the methodologies employed by the Library to systematically identify and assess its diverse range of internal systems, operational processes, and user-facing services. This foundational step is crucial for establishing the necessary parameters to enable truly data-driven decision-making.
• Mechanisms for Data Collection and Integration. The paper will outline the Library’s strategic approach to establishing consistent and reliable data collection practices. This includes the complex process of integrating disparate data sources from various library systems into a unified framework, overcoming technical and conceptual challenges to create a holistic data landscape.
• Development of the Library Services Dashboard. We will illustrate the creation of this centralized, powerful tool, demonstrating how data from diverse systems and processes—ranging from resource usage to service interactions—was consolidated and visualized. Furthermore, we will detail the implementation of advanced monitoring capabilities that provide real-time insights into the Library’s web presence, underlying systems, and the performance of key services.
• Impact During the COVID-19 Pandemic. A significant portion of the paper will be dedicated to analyzing how the dashboard directly influenced library management and strategic decision-making throughout the unprecedented challenges posed by the COVID-19 pandemic. We will highlight compelling examples demonstrating the critical value of having centralized, actionable insights at hand during a global crisis, showcasing the dashboard’s role in maintaining service continuity and adapting swiftly to evolving user needs.
This detailed exploration underscores the profound and transformative potential of leveraging data within academic libraries. By embracing a strategic, data-driven approach, institutions like KAUST can not only enhance operational efficiency and resource allocation but also proactively address emerging challenges, seize new opportunities, and consistently evolve as innovative and responsive pillars of the academic community.
Additionally, the paper presents foundational case studies—including a Lean Six Sigma textbook acquisition project and the development of the Institutional Research Tracking System (IRTS) to support Open Access initiatives—that laid the groundwork for the Library’s current data-driven strategy. The paper outlines future directions, including integrating resource usage analytics further to enhance budgeting, service optimization, and evidence-based decision-making