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IMPact@SUTD, a monthly update featuring the latest published research works of SUTD faculty and researchers.
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Single-Fluorophore-Based Organic Crystals with Distinct Conformers Enabling Wide-Range Excitation-Dependent Emissions

Angewandte Chemie International Edition

ScreenLife Capture: An open-source and user-friendly framework for collecting screenomes from Android smartphones 

Behaviour Research Methods

 

Development of machine learning model for Laser Powder Bed Fusion Additive manufacturing process

Journal of Intelligent Manufacturing

SUTD Author: Liu Xiaogang

SUTD Author: Yee Andrew Z. H., Yu Ryan, Lim Sun Sun, Lim Kwan Hui, Dinh Tien Tuan Anh, Loh Lionell, Hadianto Andre and Quizon Miguel

SUTD Author: Umesh Kizhakkinan, Pham Luu Trung Duong, David W. Rosen and Nagarajan Raghavan

In this work, we reported a universal molecular platform for inducing multicolor emissions in the crystalline phase. The emission maximum can be tuned over a wide range by varying excitation wavelengths. Furthermore, a high-capacity information storage device and a finite-state machine were established for showcasing multicolor displays and information storage. --- Liu Xiaogang

Our paper describes the development of a methodological tool to better understand digital device use, through the collection and analysis of 'screenomes' - which are high-frequency in-situ screenshots of people's smartphone use. Our pilot study found that device use is extremely heterogenous, and contributes to the idea that the concept of ‘screen time’ needs to be abandoned. We also extensively discuss the implications for future research on digital media effects, as well as the ethical considerations of using intrusive methods of collecting smartphone data.  --- Andrew Yee

"The team have developed a machine learning (ML) model using Tensor Train (TT) and Gaussian Process Regression (GPR) methods to predict the temperature field at the micro-scale of the laser powder bed fusion additive manufacturing process. The ML model could achieve a computational speed-up of 104 compared to the high-fidelity powder-scale computational model with a maximum L2 (%) error value of 1.23." --- Umesh Kizhakkinan

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