TECHINNOVATION TECH OFFERS

In the mid-infrared region, gases exhibit absorption spectral features that are typically two orders of magnitude stronger compared to the near-infrared region. This makes the mid-infrared quantum cascade laser (QCL) a highly suitable choice for gas spectroscopy applications. QCLs offer several advantages, including broadband spectral coverage ranging from 3 to 25μm, narrow linewidth, compact size, and robustness, which have contributed to their popularity in various spectroscopic applications. In this context, a portable gas sensor has been developed utilizing self-developed QCL arrays, covering two specific wavelength regimes: 9-10 μm and 13-14 μm. To further enhance the detection sensitivity, an artificial intelligence (AI) algorithm has been integrated into the gas sensor. The incorporation of a hollow-core fiber as a miniaturized gas cell contributes to the overall compactness of the system. By leveraging the capabilities of QCLs, this gas sensor overcomes critical weaknesses associated with existing approaches, particularly their lack of selectivity and inability to differentiate mixtures of gases effectively. We anticipate that this technological innovation will accelerate scientific research progress and prove valuable across various industry sectors.

Fast-moving consumer goods (FMCG) and other product components come in a wide variety of shapes, sizes and packaging configurations. During the manufacture of such products, a key challenge for automation is to effectively handle and manipulate such diverse products during production or logistical processes. Users planning to automate their production lines typically have to take into consideration the use of either multiple grippers for different product types, or incorporate an automated tool changer with added complexity and cost. To address this challenge, a Singapore start-up has developed a universal soft robotic gripper designed to manipulate a wider range of product sizes by incorporating a resizeable gripper base. Gripper adjustment is automatically carried out via an integrated computer vision system thus minimizing the need for human intervention during pick-and-place processes. The gripper's soft fingers also minimize damage to products during the gripping process.

Wetting has been a major obstacle to the widespread adoption of membrane distillation (MD) technology. In MD modules, trapped water on the permeate side can only be removed by draining the modules, which can disrupt the process. The hollow fibers in typical MD units are susceptible to wetting due to a variety of factors, which can reduce their efficiency. In addition, frequent manual intervention is required to drain the water from MD modules. This technology relates to a water trap system that automates the removal of trapped water from MD modules. The Water-Trap System prevents the accumulation of liquid inside the modules, eliminating the need for manual intervention to stop the process and drain the modules. This can extend the lifespan of the membranes. The system is compatible with any type of membrane (hollow fibers or flat sheet spiral wound). A new module design with a water trap connection for drainage is also included. This new water trap design can be used to improve existing MD systems, increasing the production of pure water or valuable concentrates and extending the lifespan of MD units, especially those with membranes that are more prone to wetting

Current state-of-the-art lab-scale methods for fabricating superhydrophobic membranes for membrane distillation often involve complex surface modifications or the use of nanomaterials. However, these methods are difficult to scale up. This technology relates to a pure rheological spray-assisted non-solvent induced phase separation (SANIPS) approach to fabricate superhydrophobic polyvinylidene fluoride (PVDF) membranes. The resulting membranes have high porosity, superhydrophobicity, high liquid entry pressure, and hierarchical micro/nanostructures. They can also be easily scaled up. The spraying step caused local distortion of the membrane surface, which induced a two-stage phase inversion. This led to the formation of multilevel polymeric crystal structures. The morphological structures and other membrane properties (e.g., mechanical strength and liquid entry pressure) could be tuned by applying spraying materials with different physicochemical properties. This facile fabrication method will pave the way for the large-scale production of superhydrophobic membranes for membrane distillation.

Osteoporosis is a significant global public health concern affecting approximately 500 million people. The condition is associated with high mortality and disability rates due to osteoporotic fractures. The management of osteoporotic fractures comes at a considerable cost of SGD 11K per patient in Singapore, placing a growing burden on healthcare budgets as the aging population increases. Currently, osteoporosis is assessed by measuring bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). However, the availability of DXA machines, particularly in developing countries, is limited. Consequently, DXA examinations are not routinely ordered, resulting in orthopaedists often lacking DXA results during examinations. Therefore, an alternative method for estimating and screening osteoporosis is necessary. To address this, an automated AI system that can predict a patient's osteoporotic score by evaluating the CTI (cortical thickness index) from a plain femur X-ray scan is designed and developed. This system would provide a preliminary assessment and enable mass screening for osteoporosis.

This technology offers an automated diagnostic solution for retinal health based on fundus image and deep learning technology. The network automatically classifies fundus images of age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma and normal into abnormal and normal classes. The network also can be run on any computing platform, delivering instant results for clinicians and patients.

Diabetes is associated with macrovascular and microvascular complications, including Diabetic Foot Ulcers (DFU). To identify and manage DFU risk, diabetic patients are recommended to go for a regular foot assessment. Patients who are at‐risk diabetic foot should undergo regular podiatry evaluation, however specialised diabetes centers are currently facing high rates of ulcer recurrence. Frequent visits to these centers can strain an already overwhelmed healthcare system. The technology developer has invented an Artificial Intelligence (AI) model that is able to detect pre-ulceration. By detecting feet at risk of developing DFU, the model is able to refer patients for timely intervention before it becomes a DFU. Users only need to submit photos of their feet from different angles and an anomaly score will be calculated.

The technology developer has designed a mobile-friendly Smart Cloud-based inventory solution for users who prefer to access real-time inventory status, such as inventory transactions and inventory levels, and perform simple transactions, on the go.  Equipped with robust analytical capabilities, the solution is capable of providing data-driven recommendations based on the inventory data such as sales trends and order history. The solution is based on open-source platforms such as Google Sheets, AppSheets and Looker Studio. The solution is quick to set up and easy to implement with customisable dashboards and data columns to suit different needs. Staff can also be trained to perform simple customisation of the inventory solution for a company’s unique application. Together with an integrated demand forecasting and re-ordering support system, this solution can help businesses to effectively manage their inventory levels and optimize their supply chain. This technology offer is ideal for businesses seeking a cost-effective cloud-based inventory solution with analytical capabilities to facilitate data-driven decisions.

Offshore land reclamation has been an important strategy for Singapore to meet its land needs. However, the ultra-soft soil in the surrounding waters makes land reclamation extremely difficult. Besides, many infrastructure projects (i.e., tunnelling, deep excavation, etc.) are also challenging when encountering soft marine clay due to its poor engineering properties, such as high water content, high compressibility, and low shear strength. Currently, ordinary Portland cement (OPC) is the most common binder used for soft clay stabilisation through deep mixing or jet grouting. However, OPC is not very effective for the stabilisation of marine soft clay with high water content. In addition, the production of OPC leads to negative environmental impacts such as non-renewable resources, high energy consumption, and high carbon emissions. The technology owner has developed a sustainable novel binder, entirely from industrial by-products, that has high stabilisation efficiency for marine soft clay. Using the same binder content, the 28-day strength of the novel binder-stabilised soft clay can be 2–3 times higher than that of the OPC-stabilised clay. In addition, the novel binder has a lower cost and less environmental impact, making it an economical and sustainable alternative to OPC. This technology is available for R&D collaboration, IP licensing, and test-bedding with industrial partners in the construction and infrastructure sectors.