The holy month of Ramadan is a sacred time when millions of Muslims around the world embark on a profound spiritual journey of fasting, prayer, and reflection. But it is also a time when many face serious health risks, as going without food or water from sunrise to sunset — often in scorching heat — can lead to dangerous levels of dehydration.
Now, scientists at KAUST have found a surprisingly simple way to track the body’s water levels during fasting: by measuring how skin interacts with a touchscreen[1].
It is not only useful when fasting. The research team, led by electrical engineer Tareq Al-Naffouri and colleagues at KAUST, showed that the same method applies to athletes, who often experience dehydration due to intense exertion and fluid loss through sweat.
“It is also reasonable to expect that the approach could one day benefit other vulnerable groups, including the very old, the very young, and those with kidney disease,” says Al-Naffouri.
To demonstrate the concept, Al-Naffouri’s team used a basic capacitive sensor — the same type found in smartphone screens — capable of detecting subtle shifts in skin moisture. When a fingertip touches the sensor, it registers changes in skin capacitance, a measure of how well the skin stores electric charge, which varies with hydration levels.
The researchers suggest that this data could eventually allow people to monitor their hydration in real time — no needles, wearables, or lab work required. A quick touch could alert someone to drink water or replenish fluids before symptoms like dizziness or fatigue begin to set in.
“We envision real-time, everyday, user-friendly hydration monitoring, where users simply place their finger on their smartphone screen to assess their hydration status,” says study author Soumia Siyoucef, a former visiting student in Al-Naffouri’s research group.
To validate their technology, the KAUST team collected more than 4,000 fingertip readings from people either observing Ramadan and fasting much of the day or athletes engaged in a game of ultimate Frisbee or working out at the gym. They trained machine-learning models to convert small changes in skin conductance into a measure of the body’s water content.
When put to the test, the system delivered impressive results — accurately distinguishing between hydrated and dehydrated states up to 92 percent of the time among athletes and 87 percent among fasting individuals.
Coral reefs form a vital part of the marine ecosystem, playing host to diverse species and supporting multiple industries, including fisheries, tourism, and recreation. However, these fragile ecosystems are under increasing threat from climate change, with warming oceans increasing stress on the coral animals and their symbiotic algal partners.
A new remote sensing tool developed by KAUST researchers has created an effective and efficient method of monitoring and predicting both the scope and severity of coral bleaching in the Red Sea[1]. The tool – developed by KAUST in partnership with SHAMS, General Organization for the Conservation of Coral Reefs and Turtles in the Red Sea – could aid conservation management and policymaking by enabling targeted, integrated management strategies to prioritize specific areas for intervention. It is applicable in the Red Sea as well as across the world.
The algae living within corals share nutrients and resources, giving corals their distinctive color. When corals are under stress and competing for limited resources, they ‘kick out’ their algal partners that help with nutrition. This results in bleaching, where corals lose their pigmentation and gradually turn white. This process weakens the coral animal and leaves it more vulnerable: prolonged bleaching events can kill corals and decimate reefs.
“Monitoring the health of coral reefs amid climate change is crucial, and satellite remote sensing provides a cost-effective strategy that is more efficient than traditional field sampling, which can be time-consuming and resource-intensive,” says Elamurugu Rajadurai Pandian at KAUST, who worked on the project during his Ph.D., under the supervision of KAUST’s, Ibrahim Hoteit.
The new tool utilizes the extensive datasets collected by satellite imaging every five days. While previous studies have used satellite imaging to monitor coral bleaching events, the team took this technique a step further by including an analysis of the severity of bleaching. This means that areas can be rapidly graded according to how intense the bleaching is likely to become.
“Healthy and bleached corals reflect light differently, and satellites can detect these variations,” says Rajadurai Pandian. The researchers took advantage of these differences in color and brightness in thousands of satellite images to accurately identify bleached corals.
Firstly, they analyzed how much light was reflected from the ocean floor by both healthy and bleached corals. Then, they used a mathematical technique called the least-squares approach. This helped identify patterns in the data and accurately segregated bleached corals from healthy ones, making the overall detection rate more precise and reliable.
“The detection accuracy depends on the atmospheric correction of satellite imagery, which is complicated by the Red Sea’s proximity to deserts and frequent dust storms,” says Rajadurai Pandian. “We used an advanced algorithm that removed erroneous reflectance values caused by aerosols and particulates. This significantly improved the accuracy of satellite ocean color data retrievals in our model, particularly over the complex, reef-filled shallow waters of the Red Sea.”
Their model also has improved spatial resolution compared to previous models, providing detailed analyses of coral health every ten meters. By monitoring bleaching severity, ranging from low to high, scientists can gain deeper insights into coral resilience and recovery potential.
“By serving as an early warning system for coral bleaching, our method will enable faster responses and better conservation strategies,” concludes Hoteit. “Such high-resolution monitoring will support sustainable fisheries and tourism management while also contributing to climate change research by tracking environmental changes in marine ecosystems.”
Combining data across mismatched maps is a key challenge in global health and environmental research. A powerful modeling approach has been developed to enable faster and more accurate integration of spatially misaligned datasets, including air pollution prediction and disease mapping[1].
Datasets describing important socio-environmental factors, such as disease prevalence and pollution, are collected on a variety of spatial scales. These range from point data values for specific locations up to areal or lattice data, where values are aggregated over regions as large as countries.
Merging these geographically inconsistent datasets is a surprisingly difficult technical challenge, embraced by biostatistician Paula Moraga and her Ph.D. student Hanan Alahmadi at KAUST.
“Our group develops innovative methods for analyzing the geographical and temporal patterns of diseases, quantifying risk factors, and enabling the early detection of disease outbreaks,” says Moraga. “We need to combine spatial data that are available at different resolutions, such as pollutant concentrations measured at monitoring stations and by satellites, and health data reported at different administrative boundary levels.”
Alahmadi and Moraga developed their new model through a Bayesian approach, which is often used to integrate large spatial datasets. Bayesian inference is usually performed using Markov chain Monte Carlo (MCMC) algorithms, which explore datasets through a ‘random walk’. The algorithms decide on each next step based on the previous one until they get as close as possible to a target (or ‘posterior’) distribution. However, MCMC can take up a lot of computational time, so the researchers used a different framework called the Integrated Nested Laplace Approximation (INLA).
“Unlike MCMC, which relies on sampling, INLA uses deterministic approximations to estimate posterior distributions efficiently,” explains Alahmadi. “This makes INLA significantly faster while still providing accurate results.”
The researchers demonstrated the power of their model by integrating point and areal data in three case studies: the prevalence of malaria in Madagascar, air pollution in the United Kingdom, and lung cancer risk in Alabama, USA. In all three, the model improved the speed and accuracy of predictions while providing insight into the importance of different spatial scales.
“In general, our model gives more weight to point data because they offer higher spatial precision and are often more reliable for detailed predictions,” says Alahmadi. “In all studies, point data played a dominant role. However, the influence of areal data was greater in the air pollution study. This is primarily because the air pollution areal data had a finer resolution, which made them more informative and complementary to the point data.”
Overall, the project addresses the increasing need for data analysis tools that support evidence-based decisions in health and environmental policy. For example, if public health officials can quickly assess disease prevalence, then they can work more effectively to allocate resources and intervene in high-risk areas.
The new model could be adapted to capture dynamic changes over space and time and to address biases that may arise due to preferential sampling in certain areas. The researchers plan several other applications of their model, such as using satellite pollution data to estimate disease risks.
“We hope to combine satellite and ground-based temperature data to detect thermal extremes in Mecca, particularly during the Hajj season, where heat stress is a serious public health concern,” says Moraga. “We also intend to monitor air pollutants and track emissions, supporting Saudi Arabia’s journey toward its net-zero goals.”
An electrochemical sensor designed to address a global health issue that particularly impacts people in the Middle East and North Africa (MENA) has been created by a multidisciplinary team at KAUST. The sensor detects low vitamin D levels in blood samples, providing early warning of an essential vitamin deficiency that can have severe health consequences if left untreated[1].
“Vitamin D deficiency can result in broad health complications including cardiovascular disease, autoimmune disorders, neurodegenerative diseases and skeletal deformities,” says Sharat Chandra Barman, a postdoc in the labs of Husam Alshareef and Dana Alsulaiman, the project’s co-leaders. “Early diagnosis of vitamin D deficiency is crucial.”
The body makes vitamin D in the skin when exposed to sunlight, but in hot regions of the world people often minimize their sun exposure.
“Despite ample sunshine in Saudi Arabia and the MENA region, the prevalence of vitamin D deficiency is alarmingly high, making it a critical and often overlooked public health challenge,” Alsulaiman says. Around 80 percent of the region’s population is deficient in vitamin D, and 16 percent of people in Saudi Arabia are severely deficient.
It is difficult to measure the essential vitamin at clinically relevant concentrations in a blood sample.
“The molecule’s small size, low circulating concentrations in the blood, and its structural similarity to other biomolecules all present challenges,” Alsulaiman says. As a result, vitamin D testing is typically carried out on specialized equipment only available in large urban centers.
To create a simple yet accurate vitamin D testing device for use even in remote healthcare centers, the team created a novel electrochemical sensor that combined MXene 2D nanomaterials and vitamin D selective antibodies.
“MXenes have several features that suit biosensor applications,” Barman explains. “They are biocompatible, possess excellent electrical conductivity, and their surface is covered with tunable chemical groups that can enable further device functionality to be incorporated.”
The team used these chemical groups to attach vitamin D-binding antibodies to the MXene surface.
“Combining MXenes with antibodies resulted in a very sensitive and highly selective material for point-of-care vitamin D detection,” Barman says. The team was able to show that when the antibodies on the device bound to vitamin D, the current flow through the biosensor fell measurably, with the size of the electrical response proportional to the concentration of vitamin D in the sample.
The sensor had a vitamin D detection limit of just 1 picogram per milliliter of sample, with a dynamic range of 0.1–500 nanograms per milliliter. “This range effectively covers clinically relevant vitamin D levels, from deficiency to insufficiency, sufficiency, and toxicity ranges,” Barman says.
“The sensor also demonstrated high selectivity, showing minimal interference from non-target biomolecules like glucose, vitamin C, and vitamin B12,” he adds.
“Our synergistic combination of MXenes and antibodies enabled us to develop a biosensing platform for vitamin D deficiency that is low-cost, rapid, and decentralized – this advances accessible healthcare solutions in line with the goals of Saudi’s visionary Health Sector Transformation Program,” Alsulaiman says.
Hydrogen is a clean-burning fuel that could help to replace fossil fuels in transportation, the chemicals industry, and many other sectors. However, hydrogen is also an explosive gas, so it is essential to have safety systems that can reliably detect leaks in a variety of circumstances.
KAUST researchers have invented a robust, highly sensitive, low-cost hydrogen sensor that outperforms commercial detectors, offering a vital safeguard for the burgeoning hydrogen economy[1].
“Conventional hydrogen sensors face several limitations,” explains Suman Mandal of the Physical Science and Engineering Division at KAUST, a member of the team behind the work. “These sensors often respond slowly to hydrogen leaks, cannot detect trace levels of hydrogen, and must be heated during operation, for example.”
The researchers have overcome these problems using a semiconducting polymer called DPP-DTT, which they coated onto a pair of platinum electrodes. Exposure to hydrogen reduced the current flowing through the device by up to 10,000 times, offering a powerful detection signal, with the drop in current corresponding to the concentration of hydrogen.
“This high responsivity ensures rapid and precise detection of gas leaks, which is essential for safety in industrial and transportation sectors,” says Mandal
The device operates at room temperature and can detect traces of hydrogen at just 192 parts per billion. It responds within one second of exposure and consumes barely two microwatts of power. Laboratory tests showed the device could operate over a wide temperature and humidity range and remained functional for two years.
The researchers tested the device in various real-world scenarios, which included hydrogen leaking from a pipe and bursting hydrogen-filled balloons in a room. They even mounted the device on a drone and flew it through an area where a hydrogen leak had occurred. In all scenarios, the device performed better than commercial sensors.
The sensor could also detect hydrogen in mixtures of volatile molecules such as ethanol and acetone, and in complex gas mixtures. The sensor only failed when the atmosphere lacked any oxygen, which provided an important clue about how it works.
Oxygen from the air enters the polymer and draws electrons from the material. This increases the current flowing through the device and leaves oxygen within the polymer and on the electrodes. If there is any hydrogen in the surrounding air, it also passes through the polymer and reaches the electrodes, where it splits into hydrogen atoms that stick to the platinum’s surface. Hydrogen and oxygen atoms then combine to form water, which escapes the device. Removing this oxygen reduces the current flowing through the device, which signals the presence of hydrogen.
“This is an entirely new hydrogen sensing mechanism,” Mandal says.
Using an inexpensive screen-printing method, the sensor could be manufactured at a low cost, making it an affordable and practical way to rapidly identify hydrogen leaks.
The team has filed a patent on the work, and plans to collaborate with a company to further develop the technology. “I believe these efforts will help address hydrogen safety issues in a cost-effective and environmentally friendly manner,” says Mandal.
The need to tackle antibiotic resistance is becoming more urgent, posing threats to the health of all species, as antibiotic resistance genes (ARGs) proliferate in the wider environment. Wastewater treatment plants are a key hotspot for the spread of antimicrobial resistance, because ARGs present in waste from humans can pass through the treatment process intact and disseminate into the environment.
“Solid waste, or sludge, is a byproduct of wastewater management that is regularly discharged into the environment after wastewater is treated,” says Julie Sanchez Medina, Ph.D. student at KAUST, who is supervised by faculty member Pei-Ying Hong. “However, sludge now contains considerable numbers of ARGs, and they are free to potentially move between species in the sludge, spreading antimicrobial resistance still further.”
Now, a metagenomics study by Sanchez Medina and co-workers has demonstrated that one type of bioreactor used in some wastewater plants – anaerobic membrane bioreactors – may be better at reducing the amount of ARGs released into the environment[1].
Membrane bioreactors use a bacterial digestion process to break down organic pollutants in wastewater. This digestion process either uses aerobic bacteria (those that thrive with oxygen) or anaerobic bacteria (without oxygen). The wastewater is then filtered through a membrane to separate out the contaminants, resulting in clean fluid effluent and leaving behind the solid sludge.
“We wanted to examine whether there are differences in the amount of ARGs and antibiotic-resistant bacterial strains in the sludge produced by aerobic or anaerobic membrane bioreactors,” says Sanchez Medina.
The team compared two systems that were treating the same stream of wastewater, and collected sludge from each of them in two independent runs during a five-month period. They extracted DNA from the sludge, then sequenced and analyzed it with bioinformatic tools. The resulting datasets enabled the researchers to estimate the abundance of ARGs according to the sludge volume that was released by each bioreactor.
“We found that sludge from the anaerobic system had a lower abundance of ARGs. Crucially, there was also a lower potential for the horizontal transfer of genes to opportunistic pathogens in the anaerobic system compared to the aerobic one,” says Sanchez Medina.
The team also examined the diversity in the types of antibiotic resistance for each system. Aerobic sludge had a higher diversity of potentially harmful ARGs, including those that confer resistance to broad-spectrum antibiotics like quinolones and tetracycline. Anaerobic sludge had fewer mobile genetic elements and the conditions within the system were less conducive to gene transfer.
“Coupled with the other advantages of anaerobic membrane bioreactors – lower overall sludge waste and lower energy requirements – this technology may be especially useful in the fight against antimicrobial resistance,” notes Hong.
“We plan to conduct a further, long-term study of anaerobic versus aerobic systems to gain more insights into the risk of antibiotic resistance dissemination and the downstream impacts in terms of reusing treated wastewater, which is a vital consideration for an arid country such as Saudi Arabia,” concludes Sanchez Medina.
A new tool created by KAUST researchers helps make climate models more practical to use[1]. The tool, called an online stochastic generator, reduces the space needed to store and analyze climate data while enabling researchers to generate nearly real-time climate data, helping them understand climate change in a timely manner.
An important element in climate modeling is reanalysis data, in which observations are incorporated into a model’s predictions to improve its accuracy. Reanalysis data can be extremely large and expensive to generate, making storage a serious issue. “The storage aspect is becoming a big problem for climate research centers because they run simulations on supercomputers that take weeks or months, and then they have terabytes of data to store somewhere for future use, which has a cost. And they’re reluctant to throw that data away,” says KAUST’s Al-Khawarizmi Distinguished Professor of Statistics Marc Genton, the study’s senior author.
To address this challenge, researchers can use tools called stochastic generators. A stochastic generator represents the climate data in a statistical model, which can be used to recreate statistically similar data. “If you fit a stochastic generator to the data, you only need to store the parameters. You could throw away the data and re-simulate it at any time quickly and cheaply,” explains Genton.
Stochastic generators also enable researchers to regenerate multiple ensembles of climate data from the stored parameters. This can give climate modelers better insight into the uncertainty of the data and help them reach more accurate predictions and conclusions about how the climate works.
However, existing stochastic generators suffer from a few shortcomings. They aren’t developed with storage constraints in mind and can’t be updated live as new data come in. “Since reanalysis data can come in real-time and span a considerable number of time points, a stochastic generator for reanalysis data must address these two challenges,” explains Dr. Yan Song, the postdoc who led the study.
Together with a collaborator at Lahore University of Management Sciences, Song and Genton have developed a stochastic generator which can incorporate new data as it comes in—an online stochastic generator. Their paper describing the new stochastic generator is one of the five finalists for the ADIA Lab Best Paper Award in Climate Data Sciences for “Pioneering Solutions for a Sustainable Future.”
The generator can take data into the model sequentially as blocks, so the model’s parameters can be updated as new data come in. “Our online stochastic generator can emulate near real-time data at high resolution, so it’s suitable for reanalysis data,” says Song. “The performance of our online stochastic generator is comparable to that of a stochastic generator developed using the entire dataset at a single time.”
Processing the data in blocks also offers a way to reduce the computational load of climate models. For example, when the available computational resources aren’t enough to store and analyze a data set, it can instead be processed as a sequence of blocks. “Because it doesn’t process all the data at once, the model we’ve developed can deal with higher resolutions in both space and time,” says Genton.
The new generator can also handle multiple variables rather than just one. The team used it to analyze two different wind speed components, but it could also be adjusted for other variables. Some variables, such as precipitation, are more complicated to model and would take more work to include in the stochastic generator. The researchers plan to continue developing the generator to handle such variables.
A key challenge lies in balancing patient privacy with the opportunity to improve future outcomes when training artificial intelligence (AI) models for applications such as medical diagnosis and treatment. A KAUST-led research team has now developed a machine-learning approach that allows relevant knowledge about a patient’s unique genetic, disease and treatment profile to be passed between AI models without transferring any original data[1].
“When it comes to machine learning, more data generally improves model quality,” says Norah Alballa, a computer scientist from KAUST. “However, much data is private and hard to share due to legal and privacy concerns. Collaborative learning is an approach that aims to train models without sharing private training data for enhanced privacy and scalability. Still, existing methods often fail in heterogeneous data environments when local data representation is insufficient.”
Learning from sensitive data while preserving privacy is a long-standing problem in AI: it restricts access to large data sets, such as clinical records, that could greatly accelerate research and the effectiveness of personalized medicine.
One way privacy can be maintained in machine learning is to break up the dataset and train AI models on individual subsets. The trained model can then share just the learnings from the underlying data without breaching privacy.
This approach, known as federated learning, can work well when the datasets are largely similar, but in situations when distinctly different datasets form part of the training library, there can be a breakdown in machine-learning process.
“These approaches can fail because, in a heterogenous data environment, a local client can ‘forget’ existing knowledge when new updates interfere with previously learned information,” says Alballa. “In some cases, introducing new tasks or classes from other datasets can lead to catastrophic forgetting, causing old knowledge to be overwritten or diluted.”
Alballa, working with principal investigator Marco Canini in the SANDS computing lab, addressed this problem by modifying an existing approach called knowledge distillation (KD) with data-free relevance estimation. The latter was designed to improve the relevance of retrieved learnings, a masking process using synthetic data to filter out irrelevant knowledge, and a two-phase training strategy that integrates new knowledge without disrupting existing knowledge to avoid the risk of catastrophic forgetting.
“Mitigating knowledge interference and catastrophic forgetting was our main challenge in developing our query-based knowledge transfer approach,” says Alballa. “QKT outperforms methods like naive KD, federated learning, and ensemble approaches by a significant margin – more than 20 percentage points in single-class queries. It also eliminates communication overhead by operating in a single round, unlike federated learning, which requires multiple communication steps.”
QKT has broad applications in medical diagnosis. It could enable, for example, one hospital’s AI model to learn to detect a rare disease from another hospital’s model without sharing sensitive patient data.
It also has applications in other systems where models must adapt to new knowledge while preserving privacy, such as fraud detection and intelligent Internet-of-Things systems.
“By balancing learning efficiency, knowledge customization, and privacy preservation, QKT represents a step forward in decentralized and collaborative machine learning,” Alballa says.
Next-generation solar cells made from organic materials could soon contribute to real-world renewable energy generation, suggests an outdoor stress test conducted under intense Saudi Arabian sunlight. The long-term study showed that certain organic light-capturing materials are surprisingly resilient to light-induced ‘photodegradation’ and revealed new ways to further optimize the cells’ longevity in realistic conditions[1].
Lightweight, semi-transparent and flexible, organic solar cells (OSCs) could potentially be used in a range of situations where conventional silicon solar panels would be too heavy, rigid or opaque to be deployed. “Recently, OSCs’ solar power conversion efficiencies (PCEs) have improved rapidly, surpassing 20 percent in laboratory settings,” says Han Xu, a postdoc in the KAUST lab of Derya Baran, who led the research with then postdoc Jianhua Han. “However, there has been much less focus on improving long-term stability of OSCs, which remains a major bottleneck hindering commercial viability,” he says.
The performance of an OSC can decline precipitously when exposed to the heat, light and moisture of outdoor environments; this occurs via degradation pathways that are poorly understood. “To bridge this knowledge gap, we systematically investigated the degradation behavior of various OSCs under light, thermal stress, and outdoor conditions,” Xu says.
The researchers focused on a component of the OSC’s light-harvesting core called the polymer donor. These materials’ photodegradation pathways have rarely been studied, despite their crucial role in OSC light absorption, charge generation and transport.
The team made a series of OSCs incorporating different polymer donors and studied the impact of factors such as polymer molecular structure on OSC longevity.
The weak spot of the polymer donors’ key photodegradation, it turned out, was the side chains that branch from the polymers’ central molecular backbone. The team showed that light could knock a hydrogen atom from a side chain or break off a side chain entirely, initiating cascading damage. “This can result in by-products such as cleaved side chains, radicals, twisted polymer backbones, and cross-linked structures,” Xu says.
Some side chains, however, were far less susceptible to this form of light-driven damage than others, the team showed. “In our study, OSCs incorporating the polymer donor PCE10, which features robust side chains, achieved 91 percent of retained PCE even after seven months of outdoor stability testing,” Xu says.
“Also notable is that some OSCs can survive harsh environmental conditions over a long period of time,” Baran says. PCE10’s class-leading stability was a surprise, she adds, because previous results have shown that PCE10 is photo-unstable in air. For their outdoor testing, the team encapsulated their OSCs to exclude air and moisture from the device. Under these conditions, PCE10 proved to be remarkably resilient to degradation — despite experiencing intense sunlight and peak temperatures of over 65 degrees Celsius.
The study highlights that OSCs should be optimized not only for their initial power conversion efficiency, but also for how well that efficiency is maintained over time in outdoor environments, Baran says.
Based on their findings, the team’s next step will be to test a new set of OSCs, trialing them under various conditions that solar cells might be exposed to in different outdoor environments around the world.
By uniting different forms of the same semiconductor material, KAUST researchers have created a self-powered device that detects ultraviolet light[1]. The key part of the device is known as a phase heterojunction (PHJ), an arrangement that could open up a host of new electronics applications.
The atoms within a crystalline semiconductor can be arranged in various patterns known as polymorphic phases. Each phase offers distinct properties, such as the light wavelength it absorbs. The new device uses two phases of gallium oxide (Ga2O3), a stable and relatively inexpensive material that absorbs deep in the ultraviolet part of the spectrum.
Electronic devices often contain adjacent layers of two semiconductor materials. But marrying two phases of the same semiconductor to create a PHJ offers several advantages over the traditional approach, explains team member Yi Lu.
For instance, PHJs can avoid absorbing unwanted wavelengths of light, and they tend to create a strong electric field at the interface between the phases, which could significantly enhance the performance of devices including solar cells, transistors and photodetectors. “Growing and integrating polymorphic phases of the same material is also more straightforward and economical than combining dissimilar materials, but it is difficult to maintain a high-quality interface” says Lu.
The researchers used a method called epitaxy to prepare their PHJ. This involves firing a laser at a target to generate a stream of atoms that subsequently assemble on a substrate.
First, they grew gallium oxide’s orthorhombic phase (κ-Ga2O3) on a sapphire substrate. They used high vacuum conditions, and included some tin in the target, which helped to generate the desired phase. On top of that, they formed a layer of the monoclinic phase (β-Ga2O3), using oxygen-rich conditions to ensure the correct crystal structure.
“The main contribution of this work is the first demonstration of a phase heterojunction with a clear, atomically-sharp, and well-defined interface,” says Xiaohang Li, who led the team.
When ultraviolet light hits this PHJ, it excites electrons into a higher energy band, leaving positively-charge ‘holes’ behind in a lower-energy band. Crucially, each of these energy bands differs slightly between the two phases, which creates an electric field at the interface between the layers. This helps to quickly and efficiently separate the electrons and holes, generating a current without having to apply an external voltage — meaning the device is self-powered.
“Researchers have previously tried to demonstrate this PHJ,” says Ph.D. student and team member Patsy A. Miranda Cortez.“However, they just formed randomly distributed mixed phases, which may not be suitable for semiconductor device mass production.”
The PHJ created a current roughly 1000 times greater than similar devices that contained only a single phase of gallium oxide, and it did so much more quickly. Consequently, it could produce a much stronger detection signal in response to very weak deep ultraviolet light.
The team now plans to combine other phases of gallium oxide, and apply their PHJs to areas such as advanced imaging, energy-efficient photonics, and power electronics.
