TECHINNOVATION TECH OFFERS

Nocturia is a common urinary complaint where one experience interruption of sleep with one or more times at night to void. The current assessment of nocturia is primarily clinical and relies mainly on patients documenting their own bladder diary to complete a Frequency-Volume Chart (FVC) of volume and times of urination between a 24-to-72-hour period. This conventional way of assessment is a manual and inadequate way of diagnosing nocturia. An individual is diagnosed with nocturnal polyuria if the total urine output at night exceeds one-third of total daily output. This technology has developed a point-of-care urine osmometer for the monitoring and profiling of day- and night-time variation in urine osmolality, providing clinicians with valuable insights to facilitate more accurate, objective diagnosis and personalized treatment plans for nocturia. This device is portable and attached with a readout sensor, designed to be user friendly. The technology owner is seeking collaborations with: Medical Institutions and Healthcare Providers: To facilitate clinical validation, implementation, and integration of the portable urine osmometer into routine diagnostic practices for nocturia and other related conditions. Device Manufacturers: To miniaturize the prototype, scale up production, ensure quality control, and help bring the device to market. Research Institutes: To collaborate on further R&D , particularly in optimizing the technology and exploring additional applications, such as hydration monitoring for athletes and military personnel. Data Analytics Partners: To develop advanced software and algorithms for precise data interpretation, which can enhance the device’s diagnostic capabilities and provide more personalized treatment options.

Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder that affects motor neurons in the spinal cord and brain which causes progressive degeneration of muscle control. The onset of ALS usually starts between the ages of 40 to 70 and affects approximately two to seven individuals per 100,000 people globally, with an average survival time after diagnosis ranging from two to five years.  ALS is hard to diagnose at an early stage due to similar symptoms with other diseases and no one single test to specifically diagnose ALS.  Several diagnosis methods include electromyogram, nerve conduction study, MRI, blood and urine tests, lumbar puncture, muscle and nerve biopsy. The absence of definitive biomarkers complicates early diagnosis and monitoring of disease progression, while the subtle and variable symptoms often leads to misdiagnosis or delays in care. This research introduces a novel predictive model for ALS that leverages deep learning techniques alongside patient-derived induced pluripotent stem cells (iPSCs). By employing advanced machine learning algorithms, the study analyzes cellular and genetic data from iPSCs to uncover patterns associated with ALS progression. It also incorporates image analysis of motor neurons derived from iPSCs of both ALS patients and healthy individuals, utilizing a convolutional neural network (CNN) model that achieves classification accuracy. This innovative approach aims to deepen the understanding of ALS mechanisms and facilitate early diagnosis and personalized treatment strategies, potentially transforming the management of neurodegenerative diseases. The research institute is seeking for clinical studies partners and pharmaceutical companies for collaboration.

Phosphine oxide-based photoinitiators, such as TPO and BAPO, are commonly used in biomedical applications due to their effective polymerization properties. TPO is widely used in products like coatings, inks, and adhesives. The industry is seeking safer alternatives with an upcoming ban on TPO effective from September 2025 in Europe due to its reproductive toxicity. Current alternatives focus on either low toxicity or increased water solubility, but there is a significant gap in finding a photoinitiator that combines both. This is important for applications requiring high biocompatibility, such as biomedical devices and tissue engineering. This technology is a new generation of photoinitiators that offers significantly lower toxicity compared to TPO/BAPO, making them ideal for sensitive biomedical applications. Their enhanced water solubility allows them to integrate easily into aqueous systems without harmful solvents, supporting the demand for sustainable products. Water-soluble photoinitiators also improve biocompatibility, reducing the risk of toxicity in applications involving direct contact with biological tissues. Additionally, these photoinitiators are customizable in curing speed, depth, and substrate compatibility, making them suitable for a variety of industries including coatings, adhesives, and advanced 3D printing. The technology owner is seeking IP licensing and R&D collaborators in the biomedical field, including manufacturers of hydrogel-based products like wound healing patches, tissue scaffolds, or bioadhesives along with companies in the materials and personal space looking for safer, sustainable photoinitiator alternatives. Medical institutions that can expand on the in vitro cytotoxicity studies or translate in vitro cytotoxicity results into in vivo animal models are also of interest. 

Tactile indicators and flat tiles are typically made from porcelain-based or traditional concrete materials. Porcelain surfaces tend to be smooth and slippery, posing a safety risk for pedestrians, and they are also brittle, making them prone to damage. Traditional concrete, while more durable, is bulky and heavy, making installation challenging. Bendable concrete tactile indicators offer a solution to the drawbacks of both materials. They are slip-resistant, durable, and lightweight, making them easier to install. It utilizes high-performance fiber-reinforced concrete that is designed using the micro-mechanics guided principles. The design focuses on suitable tailoring of fiber-cementitious matrix interface that enable tensile strain-hardening characteristics similar to metal. Under normal load conditions, bendable concrete exhibits stiffness comparable to traditional concrete. However, when overloaded, instead of fracturing suddenly, it deforms while continuing to bear the load, much like ductile metals that undergo plastic deformation after yielding. This material is exceptionally tough, with a fracture toughness comparable to that of aluminum alloys. The technology owner is seeking potential partnerships for IP licensing. Potential partners include tile manufacturers and companies in related industries.

The technology owner has developed a digital identity and networking solution in a form of a digital platform. This identification and authentication platform enables the creation, management and storage of digital and physical business cards and information in a centralized convenient location. The solution allows different methods of information exchange and storage, such as NFC, QR code, OCR or manual entry. The platform also provides a portfolio storage and service marketplace to enhance modern professional interactions and activities.

Conventional semiconductor lasers, such as distributed feedback (DFB) lasers, can achieve narrow linewidths but are limited in power output. On the other hand, high-power lasers tend to suffer from broad linewidths due to multimode operation and thermal effects. Narrow-linewidth lasers often rely on external optical feedback systems to reduce frequency range to the kHz or sub-kHz range to increase its precision. While effective, these systems add complexity and cost, requiring precise optical alignment. When the cavity size of conventional semiconductor lasers is increased, multimode lasing typically occurs, which broadens the frequency range and lowers precision as the power output rises. As a result, high-power, narrow frequency range lasers face challenges in scalability due to issues like thermal effects, multimode operation, or the reliance on external stabilization systems. The technology owner have developed a photonic-crystal surface-emitting lasers (PCSELs) for optical systems that provides high power output (up to 10W) with narrow intrinsic linewidths (~1 kHz), a performance that conventional semiconductor lasers cannot achieve without external stabilization systems. This technology solution intrinsically able to achieve kHz-class linewidth without the need for external feedback systems, simplifying the design and eliminating the need for complex setups. The photonic crystal design enables single-mode lasing over a large lasing area (1mm in diameter) without compromising on beam quality or frequency spread. This allows PCSELs to be used in high power and high precision applications, such as free-space optical communication and spaceborne LiDAR systems. The technology owner has demonstrated that by scaling up the lasing area, even higher power and narrower linewidths (<1 kHz) could potentially be achieved. The technology owner is seeking collaboration opportunities with industrial partners looking to explore this next generation optical system for laser and communication applications.

This invention addresses a significant challenge in the field of plastic resin identification and sorting, a critical issue in material recovery facilities (MRFs) and industrial plastic sorting. Traditional methods of sorting plastic resins are often inefficient and prone to errors, leading to contamination and reduced quality of recycled materials. This technology introduces a novel AI training method specifically designed for plastic resin classification using near-infrared (NIR) spectroscopy. The approach leverages self-supervised learning and masked signal modeling (MSM) to enhance the accuracy and robustness of deep learning models in identifying various plastic resins, based on their spectral signature data. One of the unique aspects of this technology is its integration with a rotary sorting system, which significantly improves the speed and precision of sorting operations in MRFs. By automating the resin identification process with accuracy of up to 95% and reducing reliance on manual sorting, this technology helps facilities achieve higher purity in recycled materials, addressing a critical need in the recycling industry. The technology owner is seeking to collaborate with industry partners operating MRFs and uses a rotary sorting system to integrate and perform test-bedding of the technology.

Access to clean and safe drinking water is a critical issue in many parts of Asia, particularly in rural and less accessible regions. A large portion of the population relies on surface or groundwater for daily consumption, yet as many as 240 million people are exposed to water that exceeds World Health Organization (WHO) safety limits. The increasing contamination of water sources due to anthropogenic activities such as industrial pollution, agricultural runoff, and inadequate sanitation has made water treatment essential. However, most portable water treatment systems currently available lack a vital feature: real-time monitoring of the treated water’s quality. This leaves consumers uncertain about whether the water they are drinking is truly safe, especially in unpredictable environments where water quality can fluctuate.  This technology combines IoT technology with water monitoring, offering real-time monitoring and feedback on water quality. This portable system allows users to remotely control and manage the treatment process, ensuring operational efficiency even in rural areas. With water-saving features and a low-maintenance design, it provides a sustainable and reliable solution for safe drinking water in remote and resource-limited regions.  The technology owner seeks collaboration with end users like rural communities, humanitarian organizations, and government agencies focused on water quality. They are also looking for test-bedding partners such as environmental research institutions and NGOs, and solution providers like manufacturers and IoT developers interested in sustainable water treatment and international expansion. 

In the preparation of traditional Asian dishes such as soups, curries, and noodles, achieving the perfect balance of saltiness is crucial for flavor consistency. However, many chefs, food manufacturers, and home cooks rely on subjective taste assessments, which can lead to inconsistent results. This becomes particularly challenging when scaling up for mass production or when catering to individuals with specific dietary needs. Current salinity testing methods are often cumbersome, require lab equipment, or are not tailored for quick, accurate, and portable use in kitchen environments, making it difficult to ensure the precise salt levels necessary for quality control. The technology provides a quick, accurate solution for measuring salinity in liquid foods like soups, curries, and noodles, ensuring consistent flavor balance. Its portable design and easy calibration with saline solution make it convenient for chefs, food manufacturers, and home cooks alike. The device displays precise salinity levels and uses intuitive facial icons for easy interpretation. By eliminating guesswork, the technology helps maintain flavor consistency, improves efficiency, and meets dietary requirements with precision. The technology owner is seeking partners in healthcare, catering, and food manufacturing for licensing and IP acquisition of the technology.