Sustainability

Pre-combustion, post-combustion and oxyfuel combustion capturing from power plants and other industrial scale companies are the three current carbon dioxide (CO2) capture and separation technologies. Unlike liquid and membrane adsorbents, solid adsorbents have a wider temperature range of adsorption and can be safely disposed in the environment. The use of solid adsorbents in industrial exhaust gases has shown to be a successful method of trapping concentrated CO2 for later storage rather than direct emission to the environment. Recent investigations have identified magnesium oxide based (MgO) solid adsorbents as a potential material for CO2 capture at intermediate temperatures. Furthermore, magnesium (Mg) based minerals are nontoxic, abundant materials which can be prepared in large scale at relatively low cost. Even though MgO has a high theoretical CO2 capture capacity (1100 mg CO2/g sorbent), it underperforms in practical applications due to a limiting number of active CO2 capture sites. MgO reacts with CO2 to create MgCO3 in dry, high-temperature circumstances. The formation of such MgCO3 carbonates obstructs additional carbon lattice transit leads which lowers the total CO2 capture efficiency. This technology offer is an anion doping method of MgO at room temperature to prevent the formation of MgCO3. The novel MgO-Mg(OH)2 composite nanomaterial is formed via electrospinning technology and improves the overall efficiency of MgO as a CO2 capture material.

Singapore is currently only producing 13% of its vegetable consumption. With little farming land available, Singapore relies heavily on imports from other countries. Due to increasing focus on food security, the alternative to solve land scarcity problem is to build greenhouses on concrete rooftop. Although concrete rooftop greenhouse are able to keep pests out, there is a signifcant heating problem which severely inhibits the growth of the vegetables. Therefore, there is a need for a rooftop greenhouse that is able to actively cool itself to avoid such problem. This technology offer is a modular rooftop greenhouse farming system (hydroponics) capable of producing vegetables on concrete roofs to meet the local demand while reducing over-reliance on imports. The design of the greenhouse farming system enables cooling and does not heat up, thus allowing the growth of pest-free vegetables. The system is approximately the size of a typical carpark lot (2.5 x 5 m). The production rate is 30 kg per month (2.5 x 5 m size) and requires minimal human intervention. The technology offer comprises both the farming system and its operation know-how. The modular rooftop greenhouse farming system can be set-up within 3 days or scaled-up when required with guaranteed vegetable growth. The break-even cost of one greenhouse is about 3 years. The technology owner is seeking to out-license their technology.

Manufacturing plants constantly seek opportunities to save energy, reduce cost, and be more environmentally sustainable. However, achieving these goals often requires heavy expenditure in the form of hiring teams of experienced engineers, who then perform cost-reduction tasks manually - this method is time-consuming, costly, and prone to inaccuracies due to the risk of human error.  This technology offer provides a no-code Artificial Intelligence (AI) powered platform that monitors energy consumption, carbon dioxide(CO2) emission, and operational expenditures (OPEX) in real-time. The AI engine builds a virtual cognitive model (digital twin) of a physical asset, e.g. a manufacturing plant or a piece of machinary. Simulations are carried out on the model to predict operational inefficiency i.e. high energy usage, equipment breakdown, etc. Upon detection of inefficiencies, the engine is able to suggest the best operating parameters to resolve the inefficiency.

Existing methods for load monitoring typically focus primarily on residential building data, while few look at the effectiveness of such systems for industrial or commercial buildings. Apart from the use of this technology for real-time supply-demand response, such methods can be extended for use in anomaly detection, small-scale load change detection, or an estimation of energy usage, without the associated high costs of sub-metering equipment. The proliferation of neural networks for such demanding tasks solves the computationally expensive problem of traditional methods like Hidden Markov Models (HMM) and fuzzy clustering algorithms. This technology offer is a neural network solution for residential and industrial energy management. It utilises a time-series forecasting tool to predict load, renewable energy generation, and electricity prices, without the need for costly sub-metering equipment. It is based on reinforcement learning algorithms which are trained by rewarding and penalising neural network algorithms for good or bad decisions respectively, the solution is a non-intrusive technique that helps residential and commercial end-users save on energy costs in the open energy market by scheduling their load demand for heating, ventilation, air conditioning (HVAC) systems, washing machines, and charging of their Electric Vehicles (EVs).

Different plant species have different nutrient requirements. The current practice of urban farming uses a generic hydroponic nutrient solution that is suitable to most plant types, and a crude sensing system that only measures total ion content in the solution. This approach often results in nutrients deficiency and/or overloading and hence requires consistently monitoring. Overloading of nutrients not only increases the input costs, it also results in greater quantities of contamination in effluent to be disposed after harvest.  A targeted hydroponic nutrient solution reduces the need to periodically adjust the nutrient. The technology provider has studied and formulated different nutrient recipes that had shown improved yield compared to commercial products. This ensures the best growth for each crop type. It also reduces common problem stemming from imbalanced nutrient, e.g. leaf chlorosis due to nutrient deficiency. All these translate to a better yield and a more marketable produce for the farmers. Formulations developed include Mizuna, Kale, Lettuce, Mustard, Kalian, and Caixin. The technology provider is seeking for licensing partners from the agriculture industry.

The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.   

Many active ingredients in formulated products often suffer from degradation triggered by light, heat, mechanical stress or volatile loss, as well as incompatibility issues with other ingredients or excipients. Encapsulation of actives could be a solution, however existing methods including nanoemulsion, liposomes, nanostructured lipids, spray drying involve undesirable steps that use organic solvents, surfactants, alcohols, non-biodegradable polymers and high shear processes. These undesirable steps render difficulties for economical scale-up.   The technology provider has developed a novel method for producing a biodegradable polymer that is based on green chemistry and easily scalable. Through their simple and novel nanoencapsulation process, the technology allows the encapsulation of most actives at submicron scale to form water-based formulation without the forementioned undesirable steps. This technology presents a low-cost, scalable co-block amphiphilic biodegradable polymer-based nanoencapsulation that is of superior performance and stability due to its polymeric chain entanglement and nano-sized effects.   The technology provider is seeking collaborations with partners, including  actives manufacturers and formulated products owners, who may have interests to adopt this encapsulation technology for hydrophobic actives in applications including insect repellents, pesticides, skincare, aromatherapy products, and pharmaceutical applications.  

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.

Many existing smart city solutions only show the impact of urban development, but few show the impact that urbanisation imposes on daily activities and long-term outcomes such as population obesity and job availability/accessibility. In short, such solutions show the activities e.g. large crowds are visiting the neighbourhood park, that are happening in real-time (what), the location (where), and the time that they occur (when), but do not have the ability to include data that makes it possible to explain the reason for such activities (why). In order to bring about any intervention or identify missed opportunities, understanding the reason behind such activity is vital. This technology utilises data on city infrastructure systems to help users understand how and where the built environment creates a set of physical constraints that influence what planned and unplanned activities are possible, and in turn how this influences long term outcomes including health and climate change. This technology imports, translates and combines datasets into spatialised models which are used to generate analytics outputs. These outputs include a comprehensive explanation of the way streets, pedestrian networks, public transport and land use interact with each other. In this manner, socio-economic and/or demographic datasets can be linked, enabling people and places to be combined in a single analytical model.