The COVID-19 pandemic has brought to the forefront the critical importance of thorough disinfection, particularly within hospital environments. However, traditional manual disinfection methods have inherent limitations, including labor shortages due to physical fatigue and risk of exposure to pathogens, inconsistent human performance, and difficulty in reaching obscured or hard-to-reach areas.

To address these challenges, a team of researchers from Pohang University of Science and Technology (POSTECH) has developed an “Intelligent Autonomous Wiping and UV-C Disinfection Robot” that can automate hospital disinfection processes. This innovative robot is capable of navigating through hospital environments and performing disinfection tasks with precision and consistency.

The key feature of this robot is its dual disinfection system, which combines physical wiping and UV-C irradiation to effectively remove contaminants from surfaces. The robotic manipulator uses a wiping mechanism to physically clean high-touch areas, while the UV-C light ensures thorough disinfection of hard-to-reach corners and narrow spaces.

Real-world testing at Pohang St. Mary’s Hospital validated the robot’s performance, with bacterial culture experiments confirming its effectiveness in disinfecting surfaces. Repeated autonomous operations were carried out to verify its long-term usability in clinical settings.

The significance of this technology lies in its ability to automate time-consuming and repetitive disinfection tasks, allowing healthcare professionals to devote more attention to patient care. Additionally, the robot’s precision control algorithms minimize operational failures, while its integration with a self-sanitizing station and wireless charging system ensures sustained disinfection operations.

Professor Keehoon Kim emphasized that despite COVID-19 transitioning into an endemic phase, it remains essential to prepare for future pandemics by advancing this disinfection robot technology beyond hospitals to public facilities, social infrastructures, and everyday environments to further reduce infection risks. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT).

Early Cancer Detection: New Algorithms Revolutionize Primary Care

Two groundbreaking predictive algorithms have been developed to help General Practitioners (GPs) identify patients who may have undiagnosed cancer, including hard-to-detect liver and oral cancers. These advanced models use information about a patient’s health conditions and simple blood tests to accurately predict their chances of having an undiagnosed cancer.

The National Health Service (NHS) currently uses algorithms like the QCancer scores to combine relevant patient data and identify individuals at high risk of having undiagnosed cancer, allowing GPs and specialists to call them in for further testing. Researchers from Queen Mary University of London and the University of Oxford have created two new algorithms using anonymized electronic health records from over 7.4 million adults in England.

The new models are significantly more sensitive than existing ones, potentially leading to better clinical decision-making and earlier cancer diagnosis. Crucially, these algorithms incorporate the results of seven routine blood tests as biomarkers to improve early cancer detection. This approach makes it easier for patients to receive treatment at much earlier stages, increasing their chances of survival.

Compared to the QCancer algorithms, the new models identified four additional medical conditions associated with an increased risk of 15 different cancers, including liver, kidney, and pancreatic cancers. The researchers also found two additional associations between family history and lung cancer and blood cancer, as well as seven new symptoms of concern (itching, bruising, back pain, hoarseness, flatulence, abdominal mass, dark urine) associated with multiple cancer types.

The study’s lead author, Professor Julia Hippisley-Cox, said: “These algorithms are designed to be embedded into clinical systems and used during routine GP consultations. They offer a substantial improvement over current models, with higher accuracy in identifying cancers – especially at early, more treatable stages.”

Dr Carol Coupland, senior researcher and co-author, added: “These new algorithms for assessing individuals’ risks of having currently undiagnosed cancer show improved capability of identifying people most at risk of having one of 15 types of cancer based on their symptoms, blood test results, lifestyle factors, and other information recorded in their medical records.”

The world of electronics is set to undergo a significant transformation thanks to a groundbreaking technology developed by a research team led by Professor Yong-Young Noh and Dr. Youjin Reo from POSTECH (Pohang University of Science and Technology). In collaboration with Professors Ao Liu and Huihui Zhu from the University of Electronic Science and Technology of China, the team has successfully created a novel p-type semiconducting material that promises to revolutionize next-generation displays and electronic devices.

Transistors, the microscopic components that regulate electric currents in smartphones and other devices, have traditionally been categorized as n-type (electron transport) or p-type (hole transport). While n-type transistors generally demonstrate superior performance, achieving high-speed computing with low power consumption requires comparable efficiency from p-type transistors. To address this challenge, the research team focused on developing a novel p-type semiconducting material.

Tin-based perovskites have emerged as a promising candidate for high-performance p-type transistors. However, traditional solution processes used to fabricate these materials present challenges in scalability and consistent quality. In a significant breakthrough, the team successfully applied thermal evaporation, a process widely used in industries such as OLED TV and semiconducting chip manufacturing, to produce high-quality caesium-tin-iodide (CsSnI3) semiconductor layers.

By adding a small amount of lead chloride (PbCl2), the researchers were able to improve the uniformity and crystallinity of the perovskite thin films. The resulting transistors exhibited outstanding performance, achieving a hole mobility of over 30 cm2/V·s and an on/off current ratio of 108, comparable to commercialized n-type oxide semiconductors.

This innovation not only enhances device stability but also enables the fabrication of large-area device arrays, effectively overcoming two major limitations of previous solution-based methods. Importantly, the technology is compatible with existing manufacturing equipment used in OLED display production, presenting significant potential to reduce costs and streamline fabrication processes.

“This technology opens up exciting possibilities for the commercialization of ultra-thin, flexible, and high-resolution displays in smartphones, TVs, vertically stacked integrated circuits, and even wearable electronics because low processing temperature below 300°C,” said Professor Yong-Young Noh.

This research was supported by the National Research Foundation of Korea (NRF) under the Mid-Career Researcher Program, the National Semiconductor Laboratory Core Technology Development Project, and Samsung Display.