A computer model that can reliably identify gases under a wide range of conditions could be used in automated systems that detect volatile organic compounds[1]. It has potential in sectors such as environmental monitoring, healthcare, and energy, where precise gas detection is essential for safety and efficiency.
“This work has significant implications for security and decision-making, essentially creating a sensor that confidently reports its predictions,” says KAUST’s Aamir Farooq, who led the work.
The model interprets data gathered by a technique called spectroscopy, which measures how gas molecules absorb particular wavelengths of light or microwaves, for example. This spectrum serves as a unique fingerprint for each gas. However, it is often challenging to detect the presence of one gas within a complex mixture, because features of different gas spectra can overlap and obscure one another.
That is where machine learning models can help. Machine learning involves feeding information to a computer algorithm so that it gradually ‘learns’ to recognize patterns and connections in the data. Once this training is complete, the model can then interpret entirely new data.
Researchers have previously trained machine learning models to identify the spectroscopic fingerprints of molecules, but these often failed to cope with real-world challenges such as noise, interference, and variations in pressure and temperature. “Previous models often required extensive datasets, and struggled to adapt to new conditions,” says Mohamed Sy, a PhD candidate in Farooq’s group who worked on the new model.
The KAUST team used several tactics to create a better machine learning model called VOC-certifire, which can identify gas molecules from their terahertz-frequency spectra. Although this model was trained on relatively limited amounts of data, it achieved the same level of accuracy as more data-intensive models.
The researchers started with simulated spectra of 12 volatile organic compounds, including ethanol and the toxic gas hydrogen sulfide, and used a strategy called augmentation to generate many variants of these spectra. This process simulated how the spectra would change at different temperatures and pressures, for example, and also introduced additional noise to mimic real-world readings.
The augmentation process produced a set of 12,000 spectra to train the model. “By training the model with these variations, it becomes more resilient, enabling it to generalize effectively with less data,” says Farooq.
When the team tested the model’s ability to identify spectra, they used another technique called randomized smoothing to further improve accuracy. This involved tweaking the spectrum multiple times, and asking the model to identify each variant. The gas molecule recognized in the majority of these tests is then more likely to be the correct answer — and the model can also report how confident it is in its assertion.
The team found that VOC-certifire was better than three rival models, and could even “offer a standardized level of accuracy that human experts may not consistently achieve”, says Sy. The researchers have continued to develop the model, and will report their latest work at the prestigious Neural Information Processing Systems conference in December 2024.
A study of 70 years of climate data has examined distinct drought patterns across the Arabian Peninsula — one of the driest regions in the world. Led by KAUST’s Ibrahim Hoteit, the team analysed climate data from 1951 to 2020 to map the long-term variability of droughts and explore the factors driving these changes[1]. “Understanding historic and future drought trends in the Arabian Peninsula is crucial for water resource management in agriculture and urban development,” says Hoteit.
The researchers used the Standardized Precipitation Evapotranspiration Index (SPEI), which considers both rainfall and temperature data. “Droughts are typically measured using the Standardized Precipitation Index (SPI), but in arid regions like the Arabian Peninsula, relying on rainfall alone is insufficient,” says climate change researcher and study author Md Saquib Saharwardi.
The study outlines four distinct drought regions across the Arabian Peninsula, each exhibiting unique seasonal drought variability. However, the KAUST team found that, overall, droughts have worsened over the past two decades, not because of a decrease in rainfall, but rather due to rising temperatures.
To predict future droughts, Hoteit and colleagues developed a machine learning approach that considers the Atlantic Multidecadal Oscillation (AMO), a 70-year cyclical phenomenon of the Atlantic’s sea surface temperature that significantly affects the region’s climate[2]. Interestingly, their model predicts a decrease in drought severity as the AMO shifts from a positive to a negative phase.
“During the positive phase of the AMO, droughts tend to worsen, while the negative phase leads to a reduction in droughts,” Saharwardi explains. “Our machine learning model predicts a substantial decrease in droughts over the next 20 to 30 years, coinciding with the expected negative phase of the AMO.”
This detailed analysis of historical drought patterns, long-term trends and future predictions across the Arabian Peninsula offers valuable insights for policymakers and administrators in Saudi Arabia to manage regional water resources better. The findings will also be particularly important for ongoing and future gigaprojects, such as the Saudi Green Initiative and the Red Sea Global projects, under the Saudi Vision 2030. ”Our findings enhance drought prediction accuracy while also guiding the development of climate-resilient infrastructure projects that can withstand future droughts,” says Hoteit.
The study was carried out in the Climate Change Center (CCC), the result of a strategic partnership between KAUST and the National Center for Meteorology, and provides a basis for the development of the first national drought monitoring system for the Ministry of Environment, Water and Agriculture (MEWA). “We are planning to launch this system during COP16, marking a significant milestone in climate services for the Kingdom,” he adds. Looking ahead, the team plans to expand their research using advanced numerical climate models developed at KAUST to generate high-resolution regional climate change scenarios for the period spanning 1980-2100. “These model outputs will be instrumental for policymakers and planners to take informed action for mitigating the impact of droughts and ensure sustainable economic growth in the face of environmental changes,” Hoteit concludes.
Theoretical calculations by KAUST researchers have identified a material that could improve magnetic data-storage devices. This material consists of just three atomic layers and could lead to smaller and more reliable magnetic tunnel junctions (MTJs)[1].
MTJs contain two ferromagnetic layers separated by a thin insulating barrier, which enables them to sense magnetization. They are used, for example, to read data from hard disk drives that store the zeroes and ones of binary information in the magnetization of tiny regions on its disk.
MTJs also form the basis of a data storage technology called magnetic random-access memory, which is used in applications where data must be read or written rapidly, or when high endurance is required. In these MTJs, one of the ferromagnetic layers has a fixed magnetization, while the magnetization of the other can point in the same or the opposite direction. These two states encode the binary information. Data is written by switching the magnetization, and it can be read by determining the electrical resistance between the layers.
Conventional MTJs suffer from tiny defects that limit their performance, so researchers are trying to develop more advanced devices based on 2D materials, which may contain only a few atomic layers.
The KAUST team has explored an MTJ design that uses two pieces of the 2D material lanthanum iodide as its ferromagnetic layers. The space between these layers — known as a van der Waals gap — acts as an insulating barrier. The lanthanum iodide layers are sandwiched between electrodes made from graphene, a honeycomb lattice of carbon atoms. The researchers’ calculations show that switching this MTJ between its two magnetic states — using an external magnetic field — alters the flow of charge between the electrodes.
This effect relies on the fact that electrons have their own intrinsic magnetism. When the magnetizations of the lanthanum iodide layers point in the same direction, electrons can pass more easily across the insulating barrier than when the magnetizations of the layers point in opposite directions.
The difference in the electrical currents of the device’s two states determines the tunneling magnetoresistance ratio. A high ratio implies more reliable switching between these states. The researchers calculate that their device has a very high tunneling magnetoresistance ratio of 653%, more than three times that of commercial iron-based MTJs.
“The key advantage over conventional magnetic tunnel junctions is the use of a 2D material, which enables an ultra-thin design with atomically smooth interfaces,” says Udo Schwingenschlögl, who led the team. Exploiting the van der Waals gap between the two ferromagnetic layers also simplifies the device design, he adds, because no extra insulating layer is required.
“A high magnetoresistance ratio can also improve the reliability of the data storage in magnetic random-access memories,” says Shubham Tyagi, a Ph.D. student in KAUST’s Physical Science and Engineering Division, who was part of the team. The researchers now plan further calculations for different combinations of 2D materials and hope to discover arrangements that enhance the performance of MTJs even further.
Climate change presents formidable, complex challenges for urban planners and policymakers around the world, from protecting the public from health risks to managing the threats posed by extreme weather events. To address these challenges and offer advice and solutions, Sami G. Al-Ghamdi, founder of the Urban Lab at KAUST, has compiled and edited a book entitled ‘Sustainable Cities in a Changing Climate: Enhancing Urban Resilience’[1].
The book offers a comprehensive guide for urban planners, policymakers and stakeholders, addressing the pressing challenges posed by climate change. Al-Ghamdi aims to provide practical tools and strategies to build resilience against climate threats, such as extreme weather and public health risks.
“Cities are facing unprecedented pressures, including rising temperatures, flash flooding, increasing incidence of vector-borne diseases and sea level rise. These challenges threaten the long-term sustainability of many cities and the well-being of their inhabitants,” says Al-Ghamdi. “By bringing together authors with a diverse range of expertise, this book aims to provide a comprehensive and timely exploration of the critical intersection between urban development, climate change and resilience.”
Al-Ghamdi’s own interdisciplinary research forms the basis for many of the themes touched on in the book.
Supporting the Middle East and beyond
The Middle East is one of the most water scarce regions in the world, and desertification and extreme heat present a significant threat to livelihoods and public health, with elderly and vulnerable people particularly at risk. Efficient and sustainable water management is crucial. So is the need for resilient energy systems — that power air-conditioning, for instance — highlighting the need to build and maintain a disruption-free energy network.
“The Middle East’s reliance on energy-intensive desalinated water is unsustainable,” says Al-Ghamdi. We need to adopt energy-efficient technologies, increase wastewater recycling and raise awareness about the environmental impacts of desalinated water consumption. Our energy networks also need to work seamlessly with all forms of energy generation.”
Effective management of the rapid growth and spread of urban areas is also important globally. Urban planners must “expect the unexpected” and, for example, ensure that built environments can cope with the intensifying storms and heavy rainfall in the MENA region.
“During rainstorms in Saudi Arabia, our coastal cities can act as dams that block water runoff, resulting in severe flash flooding and danger to life,” says Al-Ghamdi. “We need natural solutions for absorbing and diverting water to prevent flooding, hence the need for green-blue-grey infrastructure (GBGI).”
Natural flood management solutions
GBGI is a comprehensive solution for sustainable urban flood risk management. It involves combining green infrastructure such as parks and green roofs, blue infrastructure including rivers and wetlands, and grey infrastructure (stormwater drains, retention basins). By integrating these elements, cities can better manage flood risks while enhancing urban resilience.
“Such systems help regulate urban temperatures, manage stormwater and reduce flood risks. For example, Copenhagen’s Cloudburst Management Plan incorporates GBGI to handle extreme rainfall events,” says Mohammad M. Al-Humaiqani, a postdoc at Urban Lab. “GBGI also improves urban biodiversity, enhances recreational spaces and adapts to a rapidly evolving climate challenge.”
Helping urban communities adapt to climate change is an important strand throughout the book. Al-Ghamdi emphasises the importance of involving everyone in local planning and green initiatives, for example through workshops, education programs and participatory planning sessions in local communities.
Developing sustainable energy systems
Diversifying from fossil fuels to renewables and improving energy efficiency are pertinent goals for many countries. In regions like the Middle East, energy demand is high and climate conditions are harsh, adding extra pressure on the energy system.
“Policymakers should promote the development of decentralized energy systems, such as microgrids, that operate independently during disruptions,” says Al-Ghamdi. “Local microgrids specific to individual cities or even critical facilities like hospitals can maintain power when the wider national grid is under pressure.”
Boosting healthcare resilience
Al-Ghamdi’s book also covers climate-related public health challenges. It highlights the importance of developing local heat action plans, like the one implemented in Ahmedabad in India that includes an early warning system and a public education scheme, and modifying healthcare facilities to cope with heatwaves.
“Integrating climate considerations builds a resilient healthcare sector capable of withstanding multifaceted threats, thus safeguarding communities in the long term,” says Furqan Tahir, a postdoc at Urban Lab. “Healthcare systems need to be proactive and to foster interdisciplinary collaboration and data-driven decision-making processes.”
Another health risk posed by climate change is the rise of vector-borne diseases carried by blood-feeding insects. For example, mosquitoes are expanding their geographical range, bringing several associated diseases, such as malaria and Dengue Fever.
“In Jeddah, Dengue is now a seasonal illness,” says Al-Ghamdi. “To proactively identify future hotspots for vector-borne diseases, we need detailed climate modeling to quantify temperature, rainfall and humidity distributions on daily, monthly and yearly scales. This comprehensive data will underpin developing control and mitigation measures to be incorporated into health policies, ensuring better preparedness and response.”
Positive future planning
Al-Ghamdi is keen that urban planners and other stakeholders take a positive stance on tackling uncertainty, and place robust and rigorous research and practical tools at the heart of urban development.
“Our ambition is to inspire transformative action that ensure our urban areas not only survive but flourish amidst the evolving climate landscape,” concludes Al-Ghamdi. “This work reflects our dedication to fostering resilient, inclusive and adaptive urban communities for a better future.”
Hydrogen is a clean-burning fuel that could help to reduce fossil fuel consumption, but the flammable gas can be challenging to store. KAUST researchers have now calculated that vast amounts of hydrogen could be inexpensively stockpiled in pipes at the bottom of lakes and reservoirs, potentially boosting hydrogen’s role in tackling climate change[1].
Renewable energy sources such as solar and wind are intermittent, so any excess electricity output must be saved to fill gaps in supply. This can be achieved by powering electrolyzers that split water into hydrogen and oxygen. The hydrogen can be kept until it is needed and then fed into fuel cells to regenerate electricity; the only waste product produced is water.
Hydrogen can also be used in a range of industrial processes, offering a key benefit over storing the electricity in batteries. “Hydrogen can decarbonize sectors that electricity and batteries cannot decarbonize, including shipping, aviation, steel making and ammonia production,” says team member Julian Hunt, a research scientist at KAUST.
Relatively small quantities of hydrogen can be kept in pressurized containers, while larger amounts are stored in depleted natural gas reservoirs or underground salt caverns. Yet these sites are not widely available, and this approach can require transporting hydrogen over long distances.
The international team led by KAUST proposes that hydrogen could be stored in lakes and reservoirs close to where the gas is produced and consumed. The gas would be contained in polyethylene pipes filled with gravel to weigh them down. Crucially, the hydrogen would be at the same pressure as the surrounding water, so that as the water gets deeper, the pressure increases and the pipes’ energy storage capacity rises. Pipes at a depth of 200 meters could offer a lifetime storage cost of US$0.17 per kilogram of hydrogen, which Hunt says would be much more economical than using pressurized containers.
As a case study, the team calculated that California’s Oroville Lake, a 210-meter-deep reservoir, could offer a total energy storage capacity of 86 gigawatt-hours — enough to power 8,000 houses for a year.
The team estimates that worldwide there are 1,760 lakes and 3,403 reservoirs deep enough for this kind of system. These bodies of water could collectively store 12 petawatt-hours, amounting to 40% of global annual electricity consumption. More than 80% of that capacity would be found in the five largest lakes, including the Caspian Sea.
To ensure the pipes remained undisturbed, it would be important to monitor the movement of ships on these lakes. “If a large object were dropped from the surface, or a boat sank and hit the pipeline, it could damage the pipes and release hydrogen,” says Yoshihide Wada, who led the team. Still, any leaking hydrogen would simply bubble to the surface and dissipate harmlessly in the atmosphere, the researchers add.
Although there are no immediate plans to build a hydrogen storage system like this, the team is collaborating with KAUST colleague Thomas Finkbeiner to test a similar system in the Red Sea that will use compressed air to store energy.
A way to select a suite of mangrove bacteria that can transform plastic has been developed that potentially offers a new strategy in the global toolkit of plastic waste cleanup. Researchers have assessed the impact of polyethylene terephthalate (PET) particles and seawater intrusion on the microbiome of mangrove soil and then experimented with an enrichment culture to select a suite of PET-transforming microbes[1].
Plastic ocean pollution is growing globally at an alarming rate, with plastic fragments found even in deep oceans far from from human habitation. Mangroves are important biodiversity hotspots that offer a range of ecosystem services but are increasingly at risk from many stressors including plastic pollution.
“Mangrove ecosystems are exposed to high levels of plastic and their soils have been reported to contain diverse microbial communities including plastic-active microorganisms,” explains Diego Javier Jiménez Avella, a research scientist in the Microbial EcoGenomics and Biotechnology Laboratory (MEGBLab) at KAUST, who led this research project. “So we thought these soils could be a good source of microbes with potential for breaking down plastics. Yet microbial diversity and metabolic activities in mangrove soils are still largely unknown.”
Analyzing the collective genomic information of two bacterial consortia showed that some bacterial species have novel enzymes capable of breaking down and transforming PET. The novel bacterial genus Mangrovimarina plasticivorans is a particularly important member of these consortia as it carries two genes that code synthesis of monohydroxyethyl terephthalates hydrolases — enzymes that are capable of degrading a PET byproduct.
These results are important as they increase our ecological understanding of PET transformation in nature and describe a novel bacterial genus and enzymes potentially capable of degrading PET. This is also the first time researchers have demonstrated that a bacterial consortia derived from mangrove soils can transform a fossil-fuel-based hydrolysable plastic.
“Engineering microbiomes to effectively transform plastics is an exciting research theme in microbiology and biotechnology,” explains Jiménez. “It is also a daunting task: bioremediation of microplastics in natural marine ecosystems is challenging due to low effectiveness, problems with scalability, testing, implementation, evaluation and legislation.”
The team’s approach to designing microbial inoculants and/or enzyme cocktails capable of accelerating PET degradation could be broadly applied using microbial inocula from a range of terrestrial and aquatic ecosystems. This in turn could identify more novel plastic-degrading microbes or enzymes.
“These laboratory-scale findings are a step to addressing plastic pollution and require further research and development — such as optimization and scalability — before they can be practically applied,” notes Alexandre S. Rosado, principal investigator at KAUST and leader of the MEGBLab.
Led by KAUST scientists, the research team — a collaboration that began in 2021 with eight institutions in Colombia, Brazil, USA, Germany, Australia, U.K. and Saudi Arabia — anticipates that broad use of this approach could help the design of efficient microbial consortia targeting plastic transformation both in the laboratory and in large-scale industrial settings.
The team are continuing to investigate the selection of plastic-transforming microbial communities from Red Sea mangroves and enzymatic activity of putative novel PET-degrading enzymes found in this study.
An underwater metasurface that performs far better than conventional resonators has been developed by Mohamed Farhat and Ying Wu from KAUST, working with colleagues from University Bourgogne Franche-Comté in France. A new pattern is helping to overcome some of the challenges of communication and sensing in water[1].
Electromagnetic waves are heavily attenuated as they pass through water, which limits their range. Instead, acoustic or sound waves provide a more viable option. But the resonators or cavities that confine and enhance acoustic waves, which are crucial for these communication systems, do not operate as well as their electromagnetic counterparts, particularly underwater, due to increased inherent losses.
“Previously, state of the art ultrasound resonators relied on conventional resonant systems, which resulted in low quality-factors or short-lived resonances that decayed only after a few cycles of oscillations. This situation is even worse underwater, where viscous damping or increased leaking create additional losses,” says Farhat.
“Our work presents a significant improvement compared to previous designs because it achieves an exceptionally high Q-factor within a simple underwater acoustic device,” explains Farhat. Waves can be trapped within a cavity and the level of confinement is quantitatively measured by its quality factor, or Q: the higher the Q, the longer the wave energy stays trapped inside.
Farhat and his co-workers developed an underwater resonator that included a metasurface: a thin silicon film imprinted with a periodic pattern that repeats over a distance shorter than the wavelength of the wave. This pattern can be engineered to control the way the metasurface interacts with the wave.
The team used a dicing machine to create an array of 0.1-millimeter slits in a silicon substrate of periodicity of 1 millimeter. The cavity consisted of two of these metasurfaces separated by a gap of 0.8 millimeter. The researchers characterized their structure by immersing it in water and using a transducer to create ultrasound waves with frequencies between 0.5 and 3 megahertz. They could then measure how the ultrasound transmitted through or reflected from the metasurface. In this way they were able to demonstrate a Q-factor of 350 for one-megahertz ultrasonic waves.
Crucial to this success was the unusual way their cavity traps the acoustic wave. In most resonators, the energy of the trapped wave needs to be less than a certain threshold. But the structure made by Farhat and team is not fully closed; the open resonator supports quasi-bound states in the continuum localized within a compact region of space even though their energy lies above the threshold.
“These so-called bound states in the continuum, firstly discovered in quantum mechanics, do not couple to the surrounding environment and hence possess a diverging quality-factor, which is a measure of the lifetime of the resonance,” explains Farhat.
“Our research advances the field of metamaterials, acoustics, and communications but also holds tremendous promise for practical applications, such as highly efficient acoustic filters, sensors, and transducers, as well as advanced communication and medical imaging systems and non-destructive testing,” concludes Wu.
A promising solar material hampered by stability problems has been fortified by a KAUST team that studied how the material degraded when exposed to air and moisture, and then tweaked its chemical composition to make it more durable[1]. This is a key step towards using the material in solar panels and other devices.
The material is known as a perovskite, a family of inexpensive compounds that are easily processed into thin films that convert light into electricity. These perovskites generally contain three types of ingredients: metals such as tin or lead; halogen ions, including bromide or iodide; and positively-charged ions such as cesium, methylammonium or formamidinium.
Lead-only perovskites typically absorb visible light, but perovskites containing a mix of tin and lead can also absorb near-infrared light. In principle, a solar cell made from a tin-lead perovskite could be teamed with a second solar cell that absorbs different wavelengths of light, forming a partnership that offers a higher power output than a conventional solar panel.
“In these ‘tandem’ solar cells, each of the layers specializes in absorbing a specific part of the solar spectrum,” explains Luis Lanzetta, one of the leading scientists behind the new research. “It means that a larger portion of the photons in sunlight can be converted into electricity.”
Meanwhile, tin-lead perovskites could also be used to detect near-infrared light in biological imaging or medical monitoring devices.
The big problem is that oxygen and moisture in the air rapidly degrade tin-lead perovskites. This process also turns some of the perovskite’s iodide into iodine, causing further damage that significantly reduces the material’s performance in less than an hour.
The KAUST researchers studied exactly how this degradation happens at the atomic level, and used that knowledge to tackle the problem. They made perovskites containing various mixtures of cesium, methylammonium and formamidinium, and found that cesium-rich blends were far more stable than the others. In contrast, methylammonium-rich formulations generated about four times as much iodine as their cesium-rich counterparts.
Computer simulations suggested that cesium can capture iodine in a way that slows perovskite breakdown. In contrast, methylammonium turns iodine into an even more active form called triiodide. “The bad news is that triiodide forms right on top of the perovskite surface, where it is in close contact with the material and able to oxidize it rapidly,” says Lanzetta.
So the researchers added a thin layer containing cesium or rubidium to the material’s surface, and included a chemical agent into the perovskite that could scavenge iodine. These tactics produced perovskite solar cells with a good efficiency of around 18%, and the scavenger ensured the cell’s performance was virtually unchanged after 2 hours exposure to the air.
The team plans to investigate other types of iodine scavenger and test different ways of incorporating it into the perovskite to improve its performance. “We believe that further optimizing of iodine-scavenging additives for perovskite solar cells will lead to highly durable technologies,” says Derya Baran, who led the team.
Two designs of frequency-locked semiconductor laser have been developed that deliver high-purity light with an ultra-narrow linewidth and exceptionally low noise. The lasers, which operate in the near-infrared or visible regions, look set to prove useful as compact, high-quality coherent optical sources that suit chip-scale integration. They suit applications such as LIDAR, atomic clocks, optical gyroscopes, metrology and microwave photonics.
“We aimed to demonstrate the versatility of low-noise semiconductor lasers by developing devices that operate effectively at two different spectral regions – 1310nm [1] and 780nm [2]. This expands their potential for broader applications in fields that require different wavelengths” explained Professor Yating Wan from KAUST, who is the corresponding author for both papers. The research emerged through collaboration between KAUST, Sandia National Labs and the University of California, Santa Barbara in the U.S. The results were recently published in Nature Photonics and Optica.
The first laser, a quantum dot (QD) laser grown directly on silicon, uses an external fiber cavity to stabilize and narrow the emission line and achieves a Lorentzian linewidth of just 16Hz – claimed to be the narrowest ever achieved for an on-chip QD laser. Its emission wavelength of 1310nm and exceptionally narrow linewidth make it well suited for constructing a highly stable microwave synthesizer.
The second laser consists of an AlGaAs distributed feedback (DFB) design, which is connected to a SiN micro-ring resonator in order to achieve self-injection locking. This resulted in a spectral linewidth of 105Hz. Its emission wavelength of 780nm aligns perfectly with optical transition of Rubidium-87, which is used in atomic optical traps and optical clocks.
Both devices are engineered for cost-effective scalable mass production. The QD lasers, grown on CMOS-compatible (001) silicon, could potentially be manufactured in foundries on 300mm-sized silicon wafers. Meanwhile, use of commercially available components in DFB lasers brings it closer to market readiness.
Both devices bring significant improvements over most semiconductor lasers that typically have much broader linewidths in the kilohertz or megahertz range. While similar narrow linewidth performance can be achieved with fiber lasers or solid-state lasers, they are much larger devices and are not amenable to chip-scale integration with optoelectronics and electronics.
“These advances bring the performance of semiconductor lasers on par with fibre and solid-state lasers: they provide a competitive alternative that combines the benefits of reduced size and cost with high performance,” commented by Artem Prokoshin, the first author of the Optica paper.
The team plans to reduce the size of devices by bringing the external locking cavities onto the same platform as the laser. “Our primary objective is to develop fully integrated narrow-linewidth lasers, explained Wan. “Currently, both devices we’ve worked on utilize external cavities – a fibre cavity or a micro-ring resonator, both of which are off-chip. Our next step is to integrate these components on-chip.”
While there is clearly more work in refining the devices, Wan says that they are already considering real applications and the commercial potential for the lasers. In particular, KAUST is collaborating with industry partners to explore the opportunities of using the QD lasers in sensors for use in autonomous equipment in the mining sector.
“The goal of this project is to develop a solid-state LIDAR prototype that integrates 3D point cloud processing possibilities,” she explained. “This prototype is specifically designed for mining operations in complex desert environments.”
A computationally efficient statistics-based approach has made it possible to emulate global climate simulations at ultra-high spatial resolution for the first time, shows research by a KAUST-led team[1].
“Climate simulations generated by Earth system models are indispensable for advancing understanding of climate processes, predicting future changes and developing strategies to address the challenges posed by climate change,” says KAUST postdoc Yan Song. “However, generating these simulations requires extensive computation, often taking weeks or months.”
Song, along with colleague Marc Genton and collaborator Zubair Khalid from Lahore University of Management Sciences in Pakistan, took a new look at the intricate Earth system models (ESMs) that describe global climate dynamics with a view to applying statistical methods to improve their efficiency.
“ESMs enable comprehensive and detailed climate simulations, but they are computationally expensive and require massive amounts of data storage, limiting their practicality for ultra-high-resolution applications,” says Song. “Leveraging statistical techniques, we constructed a practical complement for ESMs called a statistical emulator that captures intrinsic spatiotemporal structures of the ESMs and generates fast stochastic approximations.”
Generating simulations with ESMs involves iteratively solving a series of equations for each grid cell globally over time. A statistical emulator instead uses a much less complex statistical approach with trained parameters to generate a stochastic imitation of the simulation output. Then, just the emulator’s stochastic parameters require storage, instead of the full outputs of many climate simulations.
Song, Genton and Khalid’s emulator is based on a mathematical spherical harmonic transformation that converts spatial information on a sphere into the frequency domain to enable more efficient statistical analysis.
“Spherical harmonic transformations are useful for identifying dominant spatial variations and patterns, and enables analysis in the frequency domain, which significantly accelerates computations,” says Song.
Using their emulator approach, the researchers successfully produced emulations of simulated global surface temperatures from the newly published CESM2-LENS2 dataset at a daily timescale and a spatial resolution of 110 km — the first time emulations at such temporal and spatial resolution have been attempted.
Leveraging exascale computing resources, the researchers then extended their method to enable the stochastic emulation of global surface temperatures at an hourly timescale and spatial resolution of just 3 km, for which they have been recognized with a nomination for the Gordon Bell Prize — a prestigious award for an outstanding accomplishment in high-performance computing.
“The problem addressed in this work is of significant value to climate scientists,” Song says.
