Past Projects – Lean Launchpad Singapore
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Past Projects

ENGINEERING

  • 2019
    LLPSG NUS5
    Pompeii Worm

    NUS, ChBE

    Principal Investigator: Asst Prof Chen Po-Yen

    Team Members: Wang Lijiao and Dr Shifali Chatrath

    Protection against hazards, such as chemicals and fire, is essential in many industries and occupations. Classical protective clothing often relies on thick, heavy barrier layers that are not stretchable and offer little comfort. This can lead to severe limitations because of low flexibility and discomfort. In addition, some fire resistant materials are being banned or restricted as they can give off toxic fumes if they do degrade.
    Pompeii Worm has created the next-generation protective barriers using two-dimensional (2D) materials. The products are ultrathin, ultralight, and possess high stretch-ability, while offering effective chemical and fire protection.
    Pompeii Worm has two products. One is a stretchable chemical barrier based on graphene oxide. It protects against a wide spectrum of chemicals. The other is a stretchable nanoclay-based fire barrier, effective even above 1000 °C. Manufacturing these materials is highly scalable and cost-competitive to enable ease of incorporation into widely used protective gloves, clothing and coverings.

  • 2019
    LLPSG NUS5
    HYSPRA

    NUS, CA2DM

    Principal Investigator: Asst Prof Jose C.V. Gomes

    Team Members: Giulio Baldi and Philip Lim

    Raman spectroscopy is a powerful imaging tool which can provide detailed and accurate data about materials crystallinity, purity and defects concentration. Currently available imaging systems make use mostly of two acquisition techniques: point-scan and line-scan. Although very precise, these techniques are incredibly time consuming. With our technology we aim to substantially decrease the time needed to carry out Raman measurements, hence creating a lot of opportunities for the use of Raman spectroscopy in industry applications.
    Our technology (integrated in a microscope) makes use instead of wide-field imaging to gather full images of samples at different frequency intervals. This allows for the acquisition of only necessary information, thus saving time and resources. Our proposed value stands in the fact that for the same price tag of laboratory oriented Raman systems (350k SGD for a Witec Alpha 300R), we can provide an instrument as precise with the addition of scalability in speed up to 250 times faster, depending on the material under analysis.

  • 2019
    LLPSG NUS5
    SinGENE

    NUS, CEE

    Principal Investigator: Dr You Fang

    Team Members: Dr Gu Xiaoqiong, Kong Xiaolu, Dr Yang Yi

    SinGENE focuses on providing an automatic & machine-learning assisted bioinformatics analysis service to meet the high demands from customers who have limited resources to understand their next-generation sequencing (NGS) data.
    With recent advancement, NGS raw data production is no longer constrained; instead, the huge challenge has transformed into translating the genetic codes and interpreting their biological meaning (Bioinformatics). SinGENE aims to enter this rapidly advancing field with special focus on the microbiome bioinformatics analysis, which is relatively restricted and undeveloped. With our newly developed analysis platform, in which different software tools are integrated into module-based frameworks and analysis can run automatically, our analysis time can be shortened to 1/3 of the current labour-based analysis approach. Furthermore, as machine-learning is incorporated, together with our 5-years accumulated microbiome database, the SinGENE platform provides high quality results. Customers can benefit from faster and better bioinformatics analysis service from the SinGENE platform.

  • 2019
    LLPSG NUS5
    SensAI

    NUS, ECE

    Principal Investigator: Asst Prof Ang Kah Wee

    Team Members: Dr Tan Wee Chong, Lee Youngkun, Nisim Shushan

    Typically, the targeted selectivity of a conventional chemical sensor is functionalized into the device to be exclusive. Thus most of them can only be applied to measure a single type of gas or volatile organic compound (VOC). In large-scale sensing application for multiple types of gases, an integrated system with several conventional sensor units will suffer from space constraint and high power consumption issues. Maintenance cost will also be high as each unit would have a different calibration or lifetime.
    In contrast, our sensor applies a material that is highly sensitive to light and a broad variety of gases and VOCs. Selectivity is functionalized through respective AI models developed by exhaustive machine-learning in real environment settings. By having software control over the selectivity of our sensor, new sensing capabilities can be added on demand in real-time without any change in the physical hardware, which is not currently available in a conventional sensor.

  • 2019
    LLPSG NUS5
    Lean PERC Process

    NUS, SERIS

    Principal Investigator: Dr Donny Lai

    Team Members: Alexis A.C. Lacabane and Deeraj N. Nankani

    Solar photovoltaics (PV) is a fast growing market with CAGR of 24%. In 2018, the total revenue from sales of solar cells, based on the global PV installation of 105 GW, is estimated to be USD 19 billion.
    Currently, there is a global mega-trend to convert from standard BSF cell technology to the more advanced PERC cell technology (>10% relative efficiency gain), driven by both economics and geopolitical regulations. Many of the top ten PV cell manufacturers are in transition to upgrade to PERC technology which requires two expensive tools added to each BSF cell production line. However, many medium-sized BSF cell manufacturers also need to upgrade or risk falling behind the market.
    Our Lean PERC Process, using a proprietary consumable, offers a cost-effective approach for these BSF cell manufacturers to upgrade to higher efficiency PERC cell technology without acquiring expensive laser tools, thereby reducing the overall capital cost and cost of production a simplified process flow.

  • 2019
    LLPSG NUS5
    PiezoRobotics

    A*Star

    Principal Investigator: Dr Rogerio Salloum

    Team Members: Jackie Yin Ziqi and Yi Yanling

    Vibrations in modern structures such as aircraft, robots and industrial machinery, constitute a real problem that can cause undesirable noise, diminished precision and even catastrophic failures. To reduce these harmful effects, passive absorbing materials are extensively used, but can no longer follow tight technical requirements and the increasing living standards of modern society. Unlike current technology, our Smart Vibration Absorber combines vibration technology, smart materials and integrated adaptive electronics that can be used to reduce vibrations within machines or mobile structures by resonance suppression and damping. It is based on solid-state electronic materials that replace the commonly used rubbers or fluid systems, which have a short lifespan and high maintenance costs. In this way, a high vibration damping can be achieved in any frequency band and an automatic adjustment guarantees optimal performance at all times. Our goal is to contribute to the Industry 4.0 with highly precise, efficient and durable products, and with spaces with less disturbing noises.