# Singapore University of Technology and Design

• Singapore, Singapore
Recent publications
Bound-states-in-the-continuum (BIC) is an emerging concept in nanophotonics with potential impact in applications, such as hyperspectral imaging, mirror-less lasing, and nonlinear harmonic generation. As true BIC modes are non-radiative, they cannot be excited by using propagating light to investigate their optical characteristics. In this paper, for the 1st time, we map out the strong near-field localization of the true BIC resonance on arrays of silicon nanoantennas, via electron energy loss spectroscopy with a sub-1-nm electron beam. By systematically breaking the designed antenna symmetry, emissive quasi-BIC resonances become visible. This gives a unique experimental tool to determine the coherent interaction length, which we show to require at least six neighboring antenna elements. More importantly, we demonstrate that quasi-BIC resonances are able to enhance localized light emission via the Purcell effect by at least 60 times, as compared to unpatterned silicon. This work is expected to enable practical applications of designed, ultra-compact BIC antennas such as for the controlled, localized excitation of quantum emitters.
The intelligent design of catalytic materials with unique architectures has a significant impact on regulating the polysulfides (LiPSs) conversion and boosting the performance of Li − S batteries. Here, starting from 2D catalytic MoS2 nanosheets and combined with the first-principle calculations, the covalent heterojunction and S vacancy are simultaneously developed in MoS2 to regulate the electronic structure and improve the LiPSs conversion kinetics. The S vacancy and heterojunction (MoS2-x-Co9S8-y) engineering can significantly improve the electrical conductivity of MoS2 by incorporating shallow donor levels into the MoS2. Moreover, the incorporation of Co9S8-y greatly improves the chemisorption ability of heterostructure towards LiPSs. The LiPSs are preferentially adsorbed at the catalytic Mo-S-Co heterojunction, where both Li⁺ and e⁻ are easy to access. The coupled fast Li⁺/e⁻ transportation of MoS2-x-Co9S8-y enables direct and fast LiPSs “adsorption-conversion” at the catalytic Mo-S-Co heterojunction with enhanced bidirectional catalytic properties. Due to the ingenious co-engineering of S vacancy and heterointerface, the Li − S cell with MoS2-x-Co9S8-y/rGO interlayer delivers high sulfur utilization (1382.5 mAh/g at 0.1C), excellent rate capability (710.2 mAh/g at 3C), and long cycle life over 600 cycles (0.06 % capacity decay per cycle). This work demonstrates the great potential of anion deficiency and heterojunction co-construction for high-performance Li − S batteries.
Similaritons have attracted considerable attention thanks to their outstanding output performance in recent years. In this paper, we explored the entire dynamic evolution process of similaritons in an all-normal dispersion fiber laser through the time-stretch dispersive Fourier transform technique. Particularly, the narrow-band spectral filtering effect on the pulse evolution dynamics has been discovered. The “memory” ability in the relaxation oscillation state has disappeared in similariton fiber lasers compared with the traditional soliton mode-locked fiber lasers, and the build-up time is much shorter than other fiber lasers mode-locked by the nonlinear polarization evolution. Furthermore, the extinction process of the similariton fiber laser is observed and evaluated for the first time as well. An unstable time period accompanied by a high spectral peak over the spectrum is captured during the extinction process, and then the damped relaxation oscillation state is observed. These results can provide new perspectives into the ultrafast transient process of similaritons in all-normal dispersion fiber lasers and the dynamics of sophisticated nonlinear systems.
Most studies of autonomous vehicle (AV) acceptance have focused on its acceptance by the general population. There is a dearth of knowledge among users with different abilities and needs. Hence, this study addresses this gap by investigating user acceptance of shared AVs among people with different mobility and communication needs in Singapore. Understanding the perspectives of these users is critical to ensure that shared AVs services are inclusive, aiding in acceptance. The groups studied are the 1) blind and visually impaired; 2) deaf and hard of hearing; 3) individuals using mobility aids, such as wheelchair, scooter, or cane; 4) individuals with autism and their caregivers; 5) families with young children and pregnant women; 6) seniors (age 60 + ). Using an online survey (n = 300) and focus group discussions (n = 53), we found that these user groups are (i) anticipating AV in public transport with positive attitudes and emotions, and (ii) generally concerned about various aspects of safety. AV service safety and reliability are ranked as top concerns across all groups surveyed. Users who are likely to require onboard assistance prioritised ease of boarding and alighting, and all groups ranked the presence of ‘onboard service staff’ as less important. Participants identified several important service features, such as onboard safety features - especially a ‘live’ intercom, and auditory and visual cues for visually impaired and hard of hearing. Some users would also like dedicated lanes for AVs. Inclusive experiential rides on an AV will help members of these groups feel more comfortable and prepared for AVs once deployed.
Transactive Energy (TE) is envisaged as an advanced demand response (DR) variant to leverage the flexibility of distributed energy resources (DERs) for enhancing energy balance and network management in modern power systems. However, there have been limited implementations of TE frameworks for low voltage (LV) residential networks to capture the underutilised flexibility potential of DER-equipped residential prosumers. The main purpose of this paper is to identify the rationale behind this gap in light of recent advances in TE-based energy management for residential networks. As such, first, we identify the motivation and significance of the evolution of TE framework from traditional DR schemes by reviewing their relative efficacies in utilising demand-side flexibility of DER-rich residential networks for enhancing energy balance and local network management. Second, we provide an overview of the key components of the TE framework that are essential to facilitate active negotiation and trading of demand-side flexibility in residential networks. Third, we review the state-of-the-art TE methodologies and industry projects that have utilised demand-side flexibility of residential prosumers. Finally, several challenges relevant to TE frameworks in LV residential networks are identified followed by some concluding remarks at the end of the paper.
Modeling only constitutes one aspect of decision making. The prevailing limitation of applying modeling to practice is the absence of explicit consideration of uncertainties. This review paper covers uncertainty quantification (soil properties, stratification, and model performance) and uncertainty calculation with a focus on how it enhances the role of modeling in decision making (reliability analysis, reliability-based design, and inverse analysis). The key output from a reliability analysis is the probability of failure, where “failure” is defined as any condition that does not meet a performance criterion or a set of criteria. In contrast to the global factor of safety, the probability of failure respects both mechanics and statistics, is sensitive to data (thus opening one potential pathway to digital transformation), and it is meaningful for both system and component failures. Resilience engineering requires system level analysis. As such, geotechnical software can provide better decision support by computing the probability of failure/reliability index as one basic output in addition to stresses, strains, forces, and displacements. It is further shown that more critical non-classical failure mechanisms can emerge from spatially variable soils that can escape notice if the engineer were to restrict analysis to conventional homogeneous or layered soil profiles.
Random network models generated using sparse exchangeable graphs have provided a mechanism to study a wide variety of complex real-life networks. In particular, these models help with investigating power-law properties of degree distributions, number of edges, and other relevant network metrics which support the scale-free structure of networks. Previous work on such graphs imposes a marginal assumption of univariate regular variation (e.g., power-law tail) on the bivariate generating graphex function. In this paper, we study sparse exchangeable graphs generated by graphex functions which are multivariate regularly varying. We also focus on a different metric for our study: the distribution of the number of common vertices (connections) shared by a pair of vertices. The number being high for a fixed pair is an indicator of the original pair of vertices being connected. We find that the distribution of number of common connections are regularly varying as well, where the tail indices of regular variation are governed by the type of graphex function used. Our results are verified on simulated graphs by estimating the relevant tail index parameters.
Rechargeable magnesium batteries (RMBs) have been considered an attractive candidate as beyond lithium-ion battery technology due to their abundant reserves, low cost and dendrite-free deposition process. However, one of the main obstacles in utilizing RMBs as a commercial system is the sluggish diffusion kinetics of Mg ions in cathode materials owing to the high charge density and strong electrostatic interactions, thus leading to inferior magnesium-storage capability. The recent tremendous efforts on cathode materials of RMBs provide precious experience, enlightening novel material engineering associated with emerging magnesium electrochemical systems. We first elucidate the underlying battery reaction mechanisms toward rational battery designs. We then summarize the status and issues of cathode materials, present the advanced kinetics optimization strategies and make the in-depth analyses of structure-kinetics correlations for some major research breakthroughs on high-performance batteries. The future development perspectives are also prospected about battery research. This review provides significant guidelines for exploring desirable cathode materials toward advanced magnesium-based energy storage systems.
The cover image is based on the Research Article Towards fluid force estimation of a water‐jetting aerial robot with hybrid kinematics‐force model by Shawndy M. Lee et al., https://doi.org/10.1002/rob.22079.
Convolutional neural networks, in which each layer receives features from the previous layer(s) and then aggregates/abstracts higher level features from them, are widely adopted for image classification. To avoid information loss during feature aggregation/abstraction and fully utilize lower layer features, we propose a novel decision fusion module (DFM) for making an intermediate decision based on the features in the current layer and then fuse its results with the original features before passing them to the next layers. This decision is devised to determine an auxiliary category corresponding to the category at a higher hierarchical level, which can, thus, serve as category-coherent guidance for later layers. Therefore, by stacking a collection of DFMs into a classification network, the generated decision fusion network is explicitly formulated to progressively aggregate/abstract more discriminative features guided by these decisions and then refine the decisions based on the newly generated features in a layer-by-layer manner. Comprehensive results on four benchmarks validate that the proposed DFM can bring significant improvements for various common classification networks at a minimal additional computational cost and are superior to the state-of-the-art decision fusion-based methods. In addition, we demonstrate the generalization ability of the DFM to object detection and semantic segmentation.
For two-dimensional (2D) materials, piezoelectric ferromagnetism with large out-of-plane piezoresponse is highly desirable for multifunctional ultrathin piezoelectric device application. Here, we predict that Janus monolayer CrSCl is an out-of-plane ferromagnetic semiconductor with large vertical piezoelectric response and high Curie temperature. The predicted out-of-plane piezoelectric strain coefficient d 31 is −1.58 pm/V, which is higher than that of most 2D materials (compare absolute values of d 31 ). The large out-of-plane piezoelectricity is robust against electronic correlation and biaxial strain, confirming reliability of large d 31 . The calculated results show that tensile strain is conducive to high Curie temperature, large magnetic anisotropy energy, and large d 31 . Finally, by comparing d 31 of CrYX (Y = S; X = Cl, Br, I) and CrYX (Y = O; X = F, Cl, Br), we conclude that the size of d 31 is positively related to electronegativity difference of X and Y atoms. Such findings can provide valuable guidelines for designing 2D piezoelectric materials with large vertical piezoelectric response.
NDIR CO2 gas sensors using a 10-cm-long gas channel and CMOS-compatible 12% doped ScAlN pyroelectric detector have previously demonstrated detection limits down to 25 ppm and fast response time of ∼2 s. Here, we increase the doping concentration of Sc to 20% in our ScAlN-based pyroelectric detector and miniaturize the gas channel by ∼65× volume with length reduction from 10 to 4 cm and diameter reduction from 5 to 1 mm. The CMOS-compatible 20% ScAlN-based pyroelectric detectors are fabricated over 8-in. wafers, allowing cost reduction leveraging on semiconductor manufacturing. Cross-sectional TEM images show the presence of abnormally oriented grains in the 20% ScAlN sensing layer in the pyroelectric detector stack. Optically, the absorption spectrum of the pyroelectric detector stack across the mid-infrared wavelength region shows ∼50% absorption at the CO2 absorption wavelength of 4.26 μm. The pyroelectric coefficient of these 20% ScAlN with abnormally oriented grains shows, in general, a higher value compared to that for 12% ScAlN. While keeping the temperature variation constant at 2 °C, we note that the pyroelectric coefficient seems to increase with background temperature. CO2 gas responses are measured for 20% ScAlN-based pyroelectric detectors in both 10-cm-long and 4-cm-long gas channels, respectively. The results show that for the miniaturized CO2 gas sensor, we are able to measure the gas response from 5000 ppm down to 100 ppm of CO2 gas concentration with CO2 gas response time of ∼5 s, sufficient for practical applications as the average outdoor CO2 level is ∼400 ppm. The selectivity of this miniaturized CO2 gas sensor is also tested by mixing CO2 with nitrogen and 49% sulfur hexafluoride, respectively. The results show high selectivity to CO2 with nitrogen and 49% sulfur hexafluoride each causing a minimum ∼0.39% and ∼0.36% signal voltage change, respectively. These results bring promise to compact and miniature low cost CO2 gas sensors based on pyroelectric detectors, which could possibly be integrated with consumer electronics for real-time air quality monitoring.
Two-dimensional (2D) Dirac states with linear dispersion have been observed in graphene and on the surface of topological insulators. 2D Dirac states discovered so far are exclusively pinned at high-symmetry points of the Brillouin zone, for example, surface Dirac states at $$\overline{{{\Gamma }}}$$ Γ ¯ in topological insulators Bi 2 Se(Te) 3 and Dirac cones at K and $$K^{\prime}$$ K ′ points in graphene. The low-energy dispersion of those Dirac states are isotropic due to the constraints of crystal symmetries. In this work, we report the observation of novel 2D Dirac states in antimony atomic layers with phosphorene structure. The Dirac states in the antimony films are located at generic momentum points. This unpinned nature enables versatile ways such as lattice strains to control the locations of the Dirac points in momentum space. In addition, dispersions around the unpinned Dirac points are highly anisotropic due to the reduced symmetry of generic momentum points. The exotic properties of unpinned Dirac states make antimony atomic layers a new type of 2D Dirac semimetals that are distinct from graphene.
The manipulation of unactivated aliphatic C–H bonds remains one of the most challenging tasks in synthetic chemistry. Direct hydrogen atom transfer (HAT) photocatalysis is an appealing approach to this goal. However, many methods are constrained due to low catalytic efficiency. Here we report the use of a Brønsted acid to enhance the efficiency of an inexpensive organic HAT photocatalyst, eosin Y. This strategy enables valuable transformations, including alkylation, heteroarylation and fluorination, of a wide array of unactivated C(sp3)–H bonds, using the alkane substrate as the limiting reagent. The process has been applied to the late-stage functionalization of natural products and pharmaceuticals to selectively form C–H-functionalized analogues. Experimental and computational mechanistic studies show that the HAT reactivity is significantly enhanced when the sp3 oxygen atoms on eosin Y are protonated. The method has been shown to be general across different types of direct HAT photocatalysts, demonstrating its potential in native C–H bond functionalization. Functionalization of C–H bonds through direct hydrogen atom transfer (HAT) photocatalysis is an attractive synthetic reaction; however, many methods suffer from low catalytic efficiency. Now, the efficiency of direct HAT photocatalysis using photocatalyst eosin Y combined with Brønsted acids is reported, enabling the functionalization of unactivated C(sp3)–H bonds.
Motivation Pricing decisions are often made when market information is still poor. While modern pricing analytics aid firms to infer the distribution of the stochastic demand that they are facing, data-driven price optimization methods are often impractical or incomplete if not coupled with testable theoretical predictions. In turn, existing theoretical models often reason about the response of optimal prices to changing market characteristics without exploiting all available information about the demand distribution. Academic/practical relevance Our aim is to develop a theory for the optimization and systematic comparison of prices between different instances of the same market under various forms of knowledge about the corresponding demand distributions. Methodology We revisit the classic problem of monopoly pricing under demand uncertainty in a vertical market with an upstream supplier and multiple forms of downstream competition between arbitrary symmetric retailers. In all cases, demand uncertainty falls to the supplier who acts first and sets a uniform price before the retailers observe the realized demand and place their orders. Results Our main methodological contribution is that we express the price elasticity of expected demand in terms of the mean residual demand (MRD) function of the demand distribution. This leads to a closed form characterization of the points of unitary elasticity that maximize the supplier’s profits and the derivation of a mild unimodality condition for the supplier’s objective function that generalizes the widely used increasing generalized failure rate (IGFR) condition. A direct implication is that optimal prices between different markets can be ordered if the markets can be stochastically ordered according to their MRD functions or equivalently, their elasticities. Using the above, we develop a systematic framework to compare optimal prices between different market instances via the rich theory of stochastic orders. This leads to comparative statics that challenge previously established economic insights about the effects of market size, demand transformations and demand variability on monopolistic prices. Managerial implications Our findings complement data-driven decisions regarding price optimization and provide a systematic framework useful for making theoretical predictions in advance of market movements.
MXenes, a group of newly discovered two-dimensional (2D) materials since 2011, have been demonstrated with great potential in energy storage and conversion for their exceptional electrical conductivity, large surface areas, huge variety in composition and high flexibility. Up to now, the MXenes been synthesized always have surface terminal groups, such as -OH, -O or -F, which endows the materials with high hydrophilicity, rich surface chemistry to be further processed and yet challenges in restacking. As the electrochemical and electrocatalytic properties are highly relay on the material composition and surface chemical properties, hybridizing MXene with functional materials such as transitional metal chalcogenides (TMC) and transitional metal oxides (TMO) could dnot only prevents the restacking but also introduces synergistic functionalities into the composites. Therefore, elaborately design and synthesis of MXene/TMC or MXene/TMO architectures with tunable morphologies and enhanced electrochemical properties are of great importance. Herein, this review provides a comprehensive discussion on the MXene/TMC or MXene/TMO hybrids in the application for supercapacitors, secondary batteries and electrocatalysis. Specifically, the preparation methods for hybridization are presented firstly in five categories, where the characteristics of each method in relation to the structure and morphology of resulting product are compared. Advances on recent reported MXene/TMC and MXene/TMO hybrids in the application of supercapacitors, secondary batteries, and electrocatalysis are discussed in detail and their performances are also summarized. Finally, the major challenges and future prospects are also highlighted regarding improving electrode performances.
Public key cryptography is threatened by the advent of quantum computers. Using Shor’s algorithm on a large-enough quantum computer, an attacker can cryptanalyze any RSA/ECC public key and generate fake digital signatures in seconds. If this vulnerability is left unaddressed, digital communications and electronic transactions can potentially be without the assurance of authenticity and non-repudiation. In this paper, we study the use of digital signatures in 14 real-world applications across the financial, critical infrastructure, Internet, and enterprise sectors. Besides understanding the digital signing usage, we compare the applications’ signing requirements against all six NIST’s Post-Quantum Cryptography Standardization round 3 candidate algorithms. This is done through a proposed framework where we map out the suitability of each algorithm against the applications’ requirements in a feasibility matrix. Using the matrix, we identify improvements needed for all 14 applications to have a feasible post-quantum secure replacement digital signing algorithm.
This paper discusses an artificial noise‐aided intelligent reflecting surface‐MIMO–OFDM system physical layer secure communication, in which two cases for the intelligent reflecting surface reflection coefficient models are considered separately, that is unit modulus constraint for the reflection coefficients and the more practical situation of amplitude phase‐shift dependence. Then the problem of joint optimisation for the precoding matrix, artificial noise covariance matrix and intelligent reflecting surface reflection coefficient matrix to maximise the sum secrecy rate under the power constraint at the transmitter is formulated, and then an alternate optimisation‐based inexact block coordinate descent algorithm is proposed to tackle the formulated non‐convexity problem. For the problem with unit modulus constraint for the intelligent reflecting surface reflection coefficients, closed‐form solutions of the optimisation variables are obtained by utilising the Lagrange multiplier method and the complex circular manifold method. For the problem with intelligent reflecting surface reflection coefficient amplitude phase‐shift dependence, alternate optimisation‐based penalty method is used to obtain the intelligent reflecting surface optimal reflection matrix. Numerical results indicate that the algorithm for the intelligent reflecting surface reflection coefficient unit modulus constraint achieves the maximum secrecy rate, and the algorithm for the intelligent reflecting surface reflection coefficient of amplitude phase‐shift dependence has the sub‐optimal performance, and the benchmark schemes such as no intelligent reflecting surface and intelligent reflecting surface random phase shift strategies have the worst and similar performance.
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