TECH OFFERS

This technology offer is a Ultrahigh Frequency (UHF) Radio-Frequency Identification (RFID) tag antenna for use on metal structures. 2 versions are available: A compact dual-band version with folded strip structure, with a total size of only 20 mm × 30 mm × 1.5 mm. This tag can be well used in different RFID systems, which work at different UHF bands, such as European and American frequencies. The reading patterns of this tag are with different directions in two bands. A single band version with a total size of only 10 mm × 30 mm × 1.5 mm. This tag can be well used in planar as well as conformal platforms, such as metallic cylinders and bearings. Automated factories should be interested in these tags, and they can use the miniaturized tags with RFID technology to intelligently detect whether the machinery and equipment are running normally. For the dual-band UHF RFID tag antenna, the main innovation is that its size is very compact. Compared to previous compact tags, this technology has the smallest dimensions of 0.06 λ × 0.09 λ × 0.0045 λ, where λ is the wavelength of free space at 915 MHz. In addition, very different from other products, the reading patterns of this tag are different in two bands. This design is able to provide a sufficiently far identification distance ( > 7 meters in European RFID band, and > 5 meters in American RFID band) at such extremely small size, proving that this tag has very high radiation efficiency. For the single band UHF RFID tag antenna, the main innovation is that its size is greatly reduce by three new strategies. Compared to previous compact tags, this technology has the smallest dimensions of 0.03 λ × 0.09 λ × 0.0045 λ, where λ is the wavelength of free space at 915 MHz. This design is able to provide a sufficiently far identification distance ( > 5 meters) with such extremely small size, proving that this tag has very high radiation efficiency. The primary application area of this technology is industrial intelligent RFID multifunctional detection system. The anti-metal tag of this system can use this technology because this single tag can cover two UHF RFID bands simultaneously. This technology also can be well used in intelligent driving RFID positioning system in different countries. Some products about logistics RFID management, luggage RFID management, containers RFID management, and shelves RFID management can be developed at different regions. The rapid development of the Internet of Things (IOTs) has spawned a variety of new technologies and product applications. The application trends of anti-metal tags are expected to be more extensive and more suitable for multi-scenario applications in RFID market. The technology can offer some business opportunities in smart RFID logistics and smart agriculture. It has very broad prospects for the rich sensing applications of the IOTs in the near future. This design can save a lot of installation space for the rest of the industrial equipment, especially for the dual band tag which works at 2 different frequencies, with its multifunctional radiation beams. Due to its highly efficient radiation, it can reduce the input power of the transmit components of its RFID system. Other risk factors due to the continuous heating state of the system can be further reduced.  The technology owner is keen to license this technology to RFID application technology companies, including for logistics management, access control and equipment management, etc. RFID, UHF, RFID for Metal Electronics, Sensors & Instrumentation, Embedded Systems

This technology offer is an integrated, CMOS-compatible, compact device that provides dispersion compensation of dispersion in optical fibers. Dispersion impairments is a well-known problem in the transmission of high-speed data over fiber, that limits both the fiber reaches, or poses lower limits on the power required. The technology developed allows a seamless, very low loss method for compensation of fiber dispersion, providing high magnitudes of dispersion for countering dispersion in optical fibers. Without dispersion compensation, signals are susceptible to degradation from optical fiber dispersion, with the extent of degradation worsening with longer fiber reaches. Without proper dispersion compensation, transmitted data will experience high Bit Error Rates (BER) at the receiver. This technology solves this important problem and increases the fiber reaches which may be served.   The technology has the following specifications: Dispersion magnitude is scalable through appropriate design, depending on the fiber lengths that need to be compensated for. Operating wavelength is tailorable. Tunability may be introduced through thermo-optic control. Dispersion compensation is applicable for both intensity modulated direct detection modulation formats and coherent modulation formats. This device is CMOS-compatible, low loss and operates in transmission mode. May be seamlessly integrated with photonic integrated circuits. The technology owner has experimentally demonstrated that the technology works. High-speed characterization using 30 Gb/s NRZ and 30 Gbaud/s PAM4 data showed a restoration of the eye diagram that deteriorated after propagating through 2km of optical fiber. BER characterization showed a 1.3dB improvement in power penalty out of a 1.8dB degradation at the error-free (BER = 10-12) level. Scalable dispersion has also been experimentally proven. The transmission of high speed data over optical fiber is well known to be impaired by dispersion in the optical fiber. This technology provides a very low loss solution to dispersion compensation and has been shown to restore the eye diagram and improve the bit error rate of high speed data. Potential applications would be pre- or post- dispersion compensation of optical fiber dispersion. Transceivers which serve long fiber reaches and/or utilize high speed data could incorporate this technology in the transceiver chip (either transmitter or receiver), to provide an integrated, low loss, CMOS-compatible means of high quality dispersion compensation that can be easily integrated with the rest of the transceiver chip. The silicon photonics transceiver market is projected to grow to S$6.4 billion by 2026 with a compound annual growth rate of 25%. Asia Pacific is expected to show the fastest growth with increasing adoption of high-speed data systems, supportive government initiatives, and fast-growing consumer demand. Increases transceiver reaches Provides either pre- or post- dispersion compensation, with very low loss. This would allow existing transceiver products to serve longer reaches, or support higher data rates. It would also open up new opportunities for new product lines which provide integrated dispersion compensation, reducing the complexity of deployment for network operators. Very low cost, requiring only integration of the device within the transceiver chip, negligible increase in power budget. The technology owner is looking for R&D collaboration, IP licensing/acquisition, testbedding opportunities with optical transceiver companies, data center hardware companies, telecommunications companies or silicon photonics companies. Electronics, Semiconductors, Memory & Storage

Leveraging on big data, businesses in the retail sector can increase their customer-centricity; by measuring customer engagement, footfall, dwell time, age and gender, businesses can gain data-driven insights into various metrics such as sales conversion rate and reveal the underlying relationship between sales, marketing, and in-store traffic. Such insights help to make sense of the reasons some retail stores outperform others and provide a valuable means of optimising marketing, product display and sales strategies. Understanding footfall data and queue times can also help to optimise staff allocation to avoid over or understaffing and enhance the overall customer experience (CX). This solution is a plug-and-play cloud-based solution for retail analytics through human behaviour analysis. The platform works with any Internet Protocol (IP) enabled camera that supports Real-time Streaming Protocol (RTSP) and is able to perform footfall counting, dwell time measurement, and engagement count on video footage that are sent over the Internet to the cloud-based platform, this allows it to readily provide an additional layer of intelligence on existing camera infrastructure and scales to the requirements of the deployment. It provides visualisation over a web dashboard, as downloadable executive reports, or through Application Programming Interfaces (APIs) to enable actionable business intelligence insights. Traffic counting: Footfall - number of people entering a specific location Passerby - number of people that pass by but do not stop/enter a location Zone-based - number of people that enter a user-defined zone (from any direction) Engagement count - identifies a person entering a user-defined zone, and the number of people that enter and stay in that zone Time and Emotion-based Human Behaviour Analytics: Dwell time - Shopper's staying/browsing time Queue time - Average time spent waiting in a queue Engagement time - Average walking speed (velocity), stopping time within a zone Emotion - Classifies human emotion as happy, sad, neutral, angry  Demographics: Age and gender - Deduced from clothing Visualisations are presented in the form of an overlayed heat map which indicate the amount of time the customers are spending at different parts of the store - warmer colors refer to places where people linger and the cooler colored areas are the places where customers spend the least time at. Expected video resolution between 360p to 1080p (ideal), optimal operating distance between 15-20 metres, configurable via camera zoom (if available). This technology can be deployed to understand shopper in various use-cases to : Retail shops, including pop-up carts Marketing signages Shopping malls Restaurants Supermarkets Airport lounges, boarding gates, luggage carousel Entertainment venus, clubhouses Events and exhibitions Additionally, it enables the following applications: Marketing and CX Strategy: Evaluate marketing effectiveness or visitor experience within a store Operational Optimisation: Identify peak/non-peak hours, optimise manpower by allocating additional staff into high demand areas, and receive real-time alerts for specific anomalous events Leasing/Rental Calculation: Evaluate tenant mixture e.g. tenant ranking, with engagement counting, utilise footfall counting for tenant rent/leasing calculation or evaluation against past performance Cost-effective, low deployment footprint with plug-and-play analytics - leverages existing camera infrastructure; doesn't require additional hardware installation via cloud-based infrastructure Unconstrained to the type of camera (camera-agnostic); works on any RTSP-enabled camera Engagement measurement and footfall counting enable actionable business insights (providing insights on leasing, marketing, and operational efficiency improvement) Privacy-preserving - no facial recognition performed nor storage of facial data, complies with European Union GDPR The technology owner is keen to out-license to retail stores, shopping mall owners, venue owners, event and exhibition organisers or collaborate with deep technology companies to test-bed, co-develop new products/services. Business Intelligence, Video Analytics, Machine Learning, Human Traffic, Shopper Analytics Infocomm, Video/Image Analysis & Computer Vision, Big Data, Data Analytics, Data Mining & Data Visualisation, Artificial Intelligence

Due to social stigma surrounding Sexually Transmitted Diseases (STD), cost barriers, and lack of awareness, a large proportion of men suffering from male health issues avoid accessing in-person medical care, instead, many men turn to online sources such as search engines or discussion forums to seek help. This further compounds the problem as this failure to seek medical attention leads to delays in diagnosis and subsequent treatment, while misinformation and misguidance from the general public can be dangerous and life-threatening. This technology aims to address the gap between crowd-sourced diagnosis and primary healthcare practitioners through a fully anonymous, AI-driven mobile application screening tool that covers 90% of genital pathologies e.g. bumps and lesions, and certain disease and viruses, such as syphilis, herpes, Human Papillomavirus (HPV). This technology offer is Artificial Intelligence (AI) enabled mobile application that utilises a Convolutional Neural Network (ConvNet or CNN) deep learning algorithm equipped with custom-built network layers to screen for male health issues, e.g. genital warts, sexually transmitted diseases (STD) etc. Data augmentation techniques and synthetic data generation methods have been used to vary the dataset and increase and sample size for realistic model training and testing. The highly accurate model has an accuracy of 60-90% for most cases of STI/STDs. This technology offer comprises the following features: Vision-based AI algorithm which analyses a picture and detects/identifies STDs Built-in AI explainability - visualised as heatmaps that highlight the occurrence of recognised abnormalities/pathologies Synthetic data generation pipeline for data augmentation, data bias correction iOS and Android mobile applications with anonymised data acquisition Web-based integration via a suite of REST APIs is also available for developer use. To handle the issue of data privacy, the technology complies with Health Insurance Portability and Accountability Act (HIPAA) requirements and it does not associate any names, emails, or phone numbers with the collected images i.e. screening results are provided directly to the original source of the data. This technology can be applied for use in the following areas: Pre-screening prior to seeking medical attention at a primary care facility Self-serviced recovery monitoring (post-treatment) Preventive/predictive healthcare Telemedicine/remote health monitoring Additionally, the technology is also applicable for the detection and identification of certain visible dermatological conditions and oncological conditions. First in the world application of AI techniques to assist in male STD screening Highly accurate models  Low-latency screening - results returned within 3 seconds Built-in AI explainability presented through heatmaps which increases the confidence level of the end-user The technology owner is keen to work with universities, research institutes, medical institutions, clinics and digital healthcare providers to testbed the technology and provide additional data that will improve the accuracy of the CNN model.   Sexually Transmitted Disease, Convolutional Neural Network, Dermatology, Oncology Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence, Healthcare, Telehealth, Medical Software & Imaging

The latest Singapore National Population Health Survey has reported a concerning diabetes trend. From 2019-2020, 9.5% of the adults had diabetes, slightly dropping to 8.5% from 2021-2022. About 1 in 12 (8.5%) of residents aged 18 to 74 were diagnosed, with an age-standardised prevalence of 6.8% after accounting for population ageing. Among the diabetes patients, close to 1 in every 5 (18.8%) had undiagnosed diabetes, and 61.3% did not meet glucose control targets. Prediabetes is also prevalent, with 35% progressing to type 2 diabetes within eight years without lifestyle changes. Untreated Type 2 diabetes can lead to severe health issues. Tackling this challenge requires a holistic approach, focusing on awareness, early diagnosis, and lifestyle adjustments for diabetes and prediabetes. Recognising the need for innovation to address this, the technology owner develops a cost-effective and non-invasive AI-powered solution, Blood Glucose Evaluation And Monitoring (BGEM), that detects glucose dysregulation in individuals to monitor and evaluate diabetic risks. BGEM allows users to track their blood glucose levels regularly, identify any adverse trends and patterns, and adopt early intervention and lifestyle changes to prevent or delay the onset of diabetes. Clinically validated in 2022, with a research paper published in October 2023, the technology is open for licensing to senior care/home care providers, telehealth platforms, health wearables companies, and more. The BGEM technology is an end-to-end managed AI platform that leverages Photoplethysmography (PPG) enabled wearable sensors to monitor various heart rate variability (HRV) features associated with blood glucose fluctuation. The solution comprises the following features: Optimised and validated AI algorithm Mobile Demo App Including UI/UX design guideline User-friendly visualisations SaaS Scalability Security API Integration The BGEM technology offers a cost-effective, non-invasive approach to predicting an individual's diabetes risk. The applications include: Population Health Perspective: The technology leverages the high growth rate of smart wearables and hearables, presenting an opportunity to identify undiagnosed diabetes individuals within the population. Preventive Health Monitoring: With the ability to monitor blood glucose changes regularly at minimal cost, the technology empowers high-risk users to adopt a healthier lifestyle and, therefore, prevent or delay the onset of diabetes. Diabetes around the world in 2021: 537 million adults (20-79 years) are living with diabetes, 1 in 10. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is responsible for 6.7 million deaths in 2021 - 1 every 5 seconds. Diabetes caused at least USD 966 billion dollars in health expenditure – a 316% increase over the last 15 years. 541 million adults have Impaired Glucose Tolerance (IGT), which places them at high risk of type 2 diabetes. Overview of the wearable technology market: The market is projected to expand at a compound annual growth rate (CAGR) of approximately 12.5% between 2023 to 2030. Estimated to be worth USD 55.5 billion in 2022, with a projected revenue of USD 142.4 billion by 2030. Current blood glucose monitoring technologies either require finger pricking for blood extraction or the insertion of sensors into the skin and discomfort through wearing patches for extended periods. Instead, the technology uses external sensors and algorithms to detect and predict diabetes risk. No object needs to be inserted into the user's body or continuously worn throughout the day, resulting in minimal pain and discomfort. Additionally, the only equipment required for testing is the wearable device. No additional disposable equipment needles or test strips are needed, which makes blood glucose monitoring much more convenient and cost-effective than other "State-of-the-Art" solutions. The Unique Value Proposition of BGEM include: Market-ready: It is a market-ready non-invasive diabetes risk detection and prediction AI solution that leverages consumer-grade wearables to detect blood glucose dysregulation. Performance: Demonstrates outstanding prediction and detection capabilities. Cloud-based: Operates on a cloud-based platform for seamless integration. Third-party compatibility: Easily implemented with third-party devices and apps. Sustainability: Reduction in bio-medical waste such as needles, test strips etc User-friendly: Non-invasive, convenient and allows frequent measurement. Non-invasive measurement, blood glucose, diabetes mellitus, preventive healthcare, AI, ML, Wearables, PPG, Blood Glucose Monitoring, Diabetes Monitoring, Diabetes Evaluation, Non-Invasive Diabetes Monitoring, Diabetic Risk Assessment Infocomm, Artificial Intelligence, Healthcare, Diagnostics

In Singapore, more than 30,000kg of okara are generated from soya milk and tofu production. Due to the high amount of insoluble dietary fiber and a unique, poignant smell of okara, it is often discarded as a waste product. Despite okara's low palatability, it is rich in nutrients. Therefore, the technology owner has developed a cost-effective formulation to include okara in feed for abalone. The formulation can potentially be adapted and customised for other aquatic species. The technology owner is seeking potential partners to license and commercialise the technology. The technology allows for an alternative nutrient source for animal feed allowing for the sustainability of food supply and reduction of food waste. The formulation consists of a cost-effective plant-based functional ingredient, lowering the costs of feed for aquaculture farms. The nutritional composition can be tailored for different species to increase growth rates and survivability. Okara is used as a cost-effective feed for high-value abalone, a commonly cultured species of mollusc. Okara-based feed results in the beautiful purple colouration of the shell and increased growth and survivability of abalone. In comparison, the okara-based feed costs ~30% less than commercial feed used in the industry. There is potential for okara to be included in feed for other aquatic species such as shrimp and fish. The success of this method will valorise okara, transferring them into a nutrient-dense aquatic feed while promoting a more environmentally sustainable food production chain.       okara, aquatic feed Foods, Ingredients

Vision-based Artificial Intelligence (AI) models require substantial time to train, fine-tune and deploy in production. After production, this process is still required when performance degrades and re-training on a new dataset becomes necessary; this maintenance process exists throughout the model's lifetime to ensure optimal performance. Rather than embarking on the time-consuming and painful process of collecting/acquiring data to train and tune the AI model, many organisations have turned to the use of pre-trained models to accelerate the AI model development process. This technology consists of a suite of pre-trained models that are intended to detect food, human behaviours, facial features and count people. These AI models are operable on video footage and static images obtained from cameras. Models are tuned and trained on various use-cases and are accessible via API calls or embedded within software as a Software Development Kit (SDK) library. These models can be deployed as AI as a Service on Microservices platform providing customer data protection with blockchain technology. With customer protection enhanced with blockchain technology, AI Model performance can further be enhanced to meet customer requirement.   The technology consists of a suite of pre-trained AI models that provide high accuracy (over 80%) and can be further customised to improve accuracy and adapted to different use-case scenarios. Models can be integrated in the following ways:  Installed library package embedded within software on-device/on-premise HTTP-based Application Programming Interface (API) calls with video/image data to cloud-installed library package The following are the features for various AI models: Abnormal Behaviour Recognition Continuous monitoring and detection of abnormal human behaviours e.g. fighting, loitering Event Detection Recognises a variety of subjects and events e.g. sports day, graduation, wedding, festival, Christmas, from video footage Optimised for lightweight compute capability (Intel OpenVino) Food (Fresh and Packaged) Recognition Real-time detection of fresh and packaged foods Detects abnormal fresh food or defective packaged food Classifies food types e.g. lotus, spinach, cucumber, radish etc. Optimised for low compute capability Privacy-Preserving Person Recognition Privacy preserved people detection, counting and human activity recognition Images are blurred to preserve private information that can lead to personal identification (irreversible) Optimised for lightweight edge computing Free (Empty) Space Recognition Semantic segmentation to identify empty spaces Customisable for any free-space detection scenario High accuracy in night scenes Safety Monitoring Object detection with prohibited and allowed zones (e.g. person or forklift) Detects and identifies safety risks associated with safety distances Enables audible alarm systems of abnormal situations Wellbeing and Safety Detection Automatically detects and classifies nudity images from images  Enables alerts to be delivered to parent/caregiver's device Customisable to detect new categories of inappropriate content This technology offer comprises a suite of AI models for the following applications: Abnormal Behaviour Recognition Public areas or areas where social order needs to be maintained e.g. food & beverage, entertainment establishments Event Detection Automatic creation and/or organisation of media content i.e. photo classification Automated adjustment of device hardware parameters e.g. audio, colour, brightness when displaying specific types of content e.g. sports Food (Fresh and Packaged) Recognition Food stock level detection, food inventory management Automatic detection of fresh/packaged goods within a constrained area Privacy-Preserving Person Recognition Privacy protection of visual information, in high traffic areas, without deterioration of video quality Free (Empty) Space Recognition Vehicle position localisation on roads Navigation (free-space localisation) in partial/fully self-driving automotive vehicles Identification of free storage spaces in the logistics industry Safety Monitoring Automated compliance checks Workplace safety analysis and tracking Wellbeing and Safety Detection  Parental control in browsers, smartphones or other image storage devices e.g. Network Attached Storage (NAS), Solid State Drives (SSD) AI Models were rigorously tested in the fields of different scenarios. The microservice platform where AI Model ingest the visual data streams offers a secure customer data protection and privacy using blockchain technology. Making this Microservice platform capable of tracking customer’s data usage and thus ensure privacy when AI model operating on the platform are simultaneously improved using unique customer data captured on customer’s premise. Accelerate AI development - eliminate the need for dataset creation, annotation, tuning and testing Customisable AI models - fine-tuned to environment and condition Operational support to continuously improve AI accuracy from newly collected data   event detection, abnormal human behaviour recognition, safety monitoring, food package detection, food freshness, nudity detection, empty space Infocomm, Video/Image Analysis & Computer Vision, Video/Image Processing, Artificial Intelligence

In dental treatment, clear aligners are successful alternatives to the conventional fixed appliances or braces in achieving physiological orthodontic tooth movement (OTM). However, its control of dental movement is not absolute and attachments, usually made of tooth-coloured dental composite resins, are inserted at precise locations to allow the aligners to grip the teeth and guide them into their new locations. This procedure takes up clinical time and increases the cost to the clinicians. Moreover, these attachments protrude off from the surface of the teeth making the appliance obviously visible and may also potentially increase patient’s discomfort as they scratch the insides of the patient’s mouth. Upon completion of treatment, these attachments need to be removed and the enamel surfaces of the teeth may potentially be scratched or damaged. This invention introduces a Biogel material that will be applied and act as an interface layer between the clear aligner and the clinical crowns of the teeth. As a base and catalyst, the Biogel, a 2-part mixture sets into a semi-solid form after the clear aligner is inserted onto the teeth. The Biogel is a thin interface that engages the undercuts of the teeth, grips the dentition to enhance the transfer of active orthodontic forces from the clear aligners onto the teeth without the need for placement of attachments. The Biogel does not adhere to the teeth but rather the internal surfaces of the clear aligners and can be easily peeled off clean and replaced as required. The Biogel is a 2-part mixture that chemically sets within minutes upon contact. It provides sufficient curing time for the user to insert the gel onto the internal surfaces of the clear aligners. It is packaged into a dispenser with a partition that separates the 2-part mixture. A mixing tip allows the correct ratio and homogenous mix. Upon insertion of the clear aligners carrying the Biogel, a cotton bud or tissue may be used to clean off the excess that extrudes beyond the edges of the clear aligners. The final setting is achieved after a few minutes and the clear aligner is now activated with the Biogel attachments. Typically, clear aligners are worn full time except for brushing and eating, at least 20 hours a day and changed every 1- 2 week.  The Biogel can withstand normal biting forces within the clear aligners, and it does not absorb any oral fluids, food or drinks when consumed. Although the end users are orthodontic patients who are prescribed clear aligners as their modality of orthodontic treatment, the Biogel is prescribed by the dental clinicians.. The Biogel may be purchased by the clinician to be resold to the patient or may be factored into the cost of the overall treatment fee. The Biogel may also be packaged to be sold directly to the consumer whereby the dental professional provides a prescription to direct the patient to acquire it by themselves. This Biogel is compatible with any clear aligner orthodontic systems in the market. At least 80% of dentists and orthodontists worldwide provide orthodontic treatment with some form or brand of clear aligners, and this number is still growing. Attachments are required in most of these clear aligner systems, and they have various disadvantages in terms of aesthetics, costs, time consumption and potential damage to the enamel upon polishing and/or removal. This Biogel is compatible with any clear aligner orthodontic systems currently in the market and negates the need to have attachments placed. This potentially levels the playing field between leading brands of clear aligners and other newer aligner systems. Other active ingredients may also be incorporated into the Biogel. These may include fluoride releasing, anti-bacteria, and dental whitening properties that promotes oral health, reduces dental decay, and whitens teeth while undergoing orthodontic treatment. Different flavouring may also be added. According to Fortune Business InsightsTM , the global clear aligners market size was USD 2.41 billion in 2020. The market is projected to grow from $2.85 billion in 2021 to $10.04 billion in 2028. About 60-70% of the global population suffers from misaligned teeth. In North America, 40% of children are estimated to suffer from malocclusion, and more than three out of five teenagers have severe tooth displacement. The growing demand for aesthetics and surge in demand for clear aligners in orthodontic treatment is likely to fuel the clear aligners market growth. Significant technology advancement, improvement in per capita spending, the betterment of economic indicators, and increased penetration by key companies in developing regions are contributing to the markets growth rate. Developed countries are also seeing huge growth in adults seeking orthodontic treatment, which is mainly driven by the availability of products such as clear aligners. The British Orthodontic Society (BOS) reports that 75% of their members have seen an increase in patients opting for clear aligner treatment. In 2018, it was estimated that 60% of the worldwide population suffers from problems associated with misalignment of teeth, and around 300 million people could benefit from straightening their teeth. With the clear aligner market showing rapid signs of growth now and in the immediate future, this Biogel, which is compatible to any clear aligner systems in the market will see its potential growth and uptake mirrored. Current leading clear aligner brands require the placement of attachments on the teeth for it to work effectively. The Biogel interface: Improves the aligner fit but engaging the natural undercuts of the dentition Removes or reduces the need to have attachments placed. This improves aesthetics, reduces clinical chair time and clinical costs, diminishes pain from scratching of the insides of the mouth, and improves the ease of insertion and removal of the clear aligners. The reduced need for “attachment” placement and removal also reduces any potential damage to the enamel surfaces of the dentition. May contain fluoride releasing and anti-bacterial properties to prevent and reduce the incidences of dental decay while undergoing orthodontic treatment. May contain teeth whitening properties to safely whiten teeth while undergoing orthodontic treatment. Patents granted in US and China, and patent pending in Australia and EU countries. Clear aligners, physiologic tooth movement, biogel, attachments Materials, Bio Materials

To date, the current primary feedstock for plastic production is oil, which accounts for more than 850 million metric tons of greenhouse gases emissions per year. Hence, there has been an increasing demand for green plastics, which are plastic materials produced from renewable sources. This technology offer is a synthesis method of green plastics from carbon dioxide (CO2) and renewable feedstock. The green plastics produced are non-isocyanate polyurethanes (NIPUs) and can be actively tuned to be anionic, cationic, oil-soluble and cross-linkable which enables a wide range of applications. These NIPUs are non-skin irritant, have high bio-content and can possibly be made to be bio-degradable. This technology owner is looking for partners in various industries such as personal and consumer care, coatings and lubricant additives (to name a few) for further co-development of the solution. The technology owner is keen to license this technology as well. This technology offer is a synthesis process of making green plastics from CO2 and other renewable feedstock. The technical features & specifications are as follows: Non-isocyanate polyurethanes (NIPUs) Mild synthesis conditions CO2 and renewable feedstock Tuneable properties (film forming, adhesion, emulsion stabilisation, anti-redeposition, reversible cross-linking, wax inhibition and pour point depression) Non-skin irritant High bio-content (possible to be bio-degradable) This technology is applicable for those looking for green plastics. The potential applications are: Coatings (self-healable coatings and adhesives) Consumer and personal care products (oil-based film formers, pigment dispersions, wax inhibitors) Lubricant additives In the Industry there is a trend towards the avoidance of toxic chemicals such as isocyanates and phosgene in the production processes. Incorporation of renewable feedstock, CO2 and having higher bio-content in the final product and tuneable functionality will be added advantages to capture market opportunities. Use CO2 and renewable feedstocks (decarbonisation and sustainability) Mild manufacturing conditions Higher bio-content for possible biodegradation Superior and tuneable properties Renewable feedstock, Green plastics, CO2, Polyurethanes, Bio-derived Materials, Plastics & Elastomers, Chemicals, Polymers, Sustainability, Low Carbon Economy