Zhong Chen

Bio: Zhong Chen is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 80, co-authored 1000 publications receiving 28171 citations. Previous affiliations of Zhong Chen include Institute of High Performance Computing Singapore & National Institute of Education.

Analyses of multi-color plant-growth light sources in achieving maximum photosynthesis efficiencies with enhanced color qualities.

Abstract: An optimal design of light-emitting diode (LED) lighting that benefits both the photosynthesis performance for plants and the visional health for human eyes has drawn considerable attention In the present study, we have developed a multi-color driving algorithm that serves as a liaison between desired spectral power distributions and pulse-width-modulation duty cycles With the aid of this algorithm, our multi-color plant-growth light sources can optimize correlated-color temperature (CCT) and color rendering index (CRI) such that photosynthetic luminous efficacy of radiation (PLER) is maximized regardless of the number of LEDs and the type of photosynthetic action spectrum (PAS) In order to illustrate the accuracies of the proposed algorithm and the practicalities of our plant-growth light sources, we choose six color LEDs and German PAS for experiments Finally, our study can help provide a useful guide to improve light qualities in plant factories, in which long-term co-inhabitance of plants and human beings is required

Controllable fabrication of immobilized ternary CdS/Pt–TiO2 heteronanostructures toward high-performance visible-light driven photocatalysis

Abstract: Immobilized TiO2 nanotube arrays (NTAs) co-modified with Pt and CdS nanoparticles were fabricated by using the combination of photoreduction and chemical bath deposition methods. XRD, SEM, TEM, XPS, UV-Vis and EDX methods were employed to characterize the microstructure and composition of samples, and the results showed that CdS and Pt NPs were uniformly deposited on the surface of TiO2 nanotubes. The CdS/Pt/TiO2 NTAs exhibited a much higher photocatalytic activity compared to pure TiO2 NTAs and binary CdS (or Pt)/TiO2 NTAs under visible light irradiation. A kinetic study showed that the rate constants of Pt/TiO2, CdS/TiO2 and CdS/Pt/TiO2 NTAs are 0.00736, 0.01717 and 0.02077 min−1, respectively, revealing a remarkable kinetic enhancement in the ternary heteronanostructures due to the synergistic effect of the three components. Besides, the CdS/Pt/TiO2 NTAs exhibit high stability after being used 22 times. Thus we proposed that such ternary heteronanostructures show great promise as immobilized catalysts for high efficient visible-light-driven photocatalysis.

Robust Single-Shot T 2 Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network

Abstract: Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T2 mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T2 mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T2 mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based methods. The results of simulation, phantom, and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties.

A high-resolution 2D J-resolved NMR detection technique for metabolite analyses of biological samples

Abstract: NMR spectroscopy is a commonly used technique for metabolite analyses. Due to the observed macroscopic magnetic susceptibility in biological tissues, current NMR acquisitions in measurements of biological tissues are generally performed on tissue extracts using liquid NMR or on tissues using magic-angle spinning techniques. In this study, we propose an NMR method to achieve high-resolution J-resolved information for metabolite analyses directly from intact biological samples. A dramatic improvement in spectral resolution is evident in our contrastive demonstrations on a sample of pig brain tissue. Metabolite analyses for a postmortem fish from fresh to decayed statuses are presented to further reveal the capability of the proposed method. This method is a previously-unreported high-resolution 2D J-resolved spectroscopy for biological applications without specialised hardware requirements or complicated sample pretreatments. It provides a significant contribution to metabolite analyses of biological samples, and may be potentially applicable to in vivo samples. Furthermore, this method also can be applied to measurements of semisolid and viscous samples.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Graphitic Carbon Nitride (g-C3N4)-Based Photocatalysts for Artificial Photosynthesis and Environmental Remediation: Are We a Step Closer To Achieving Sustainability?

Abstract: As a fascinating conjugated polymer, graphitic carbon nitride (g-C3N4) has become a new research hotspot and drawn broad interdisciplinary attention as a metal-free and visible-light-responsive photocatalyst in the arena of solar energy conversion and environmental remediation. This is due to its appealing electronic band structure, high physicochemical stability, and “earth-abundant” nature. This critical review summarizes a panorama of the latest progress related to the design and construction of pristine g-C3N4 and g-C3N4-based nanocomposites, including (1) nanoarchitecture design of bare g-C3N4, such as hard and soft templating approaches, supramolecular preorganization assembly, exfoliation, and template-free synthesis routes, (2) functionalization of g-C3N4 at an atomic level (elemental doping) and molecular level (copolymerization), and (3) modification of g-C3N4 with well-matched energy levels of another semiconductor or a metal as a cocatalyst to form heterojunction nanostructures. The constructi...