scispace - formally typeset
Search or ask a question
Author

Zamaan Raza

Bio: Zamaan Raza is an academic researcher from National Institute for Materials Science. The author has contributed to research in topics: Glycolaldehyde & Ice crystals. The author has an hindex of 15, co-authored 22 publications receiving 662 citations. Previous affiliations of Zamaan Raza include Pierre-and-Marie-Curie University & University College London.

Papers
More filters
Journal ArticleDOI
TL;DR: The findings from this work suggest that heterogeneous ice nucleating agents may not only enhance the ice nucleation rate, but also alter the macroscopic structure of the ice crystals that form.
Abstract: It is surprisingly difficult to freeze water. Almost all ice that forms under “mild” conditions (temperatures > −40 °C) requires the presence of a nucleating agent – a solid particle that facilitates the freezing process – such as clay mineral dust, soot or bacteria. In a computer simulation, the presence of such ice nucleating agents does not necessarily alleviate the difficulties associated with forming ice on accessible timescales. Nevertheless, in this work we present results from molecular dynamics simulations in which we systematically compare homogeneous and heterogeneous ice nucleation, using the atmospherically important clay mineral kaolinite as our model ice nucleating agent. From our simulations, we do indeed find that kaolinite is an excellent ice nucleating agent but that contrary to conventional thought, non-basal faces of ice can nucleate at the basal face of kaolinite. We see that in the liquid phase, the kaolinite surface has a drastic effect on the density profile of water, with water forming a dense, tightly bound first contact layer. Monitoring the time evolution of the water density reveals that changes away from the interface may play an important role in the nucleation mechanism. The findings from this work suggest that heterogeneous ice nucleating agents may not only enhance the ice nucleation rate, but also alter the macroscopic structure of the ice crystals that form.

90 citations

Journal ArticleDOI
TL;DR: In this paper, the formation of glycolaldehyde, the simplest monosaccharide sugar, has been detected in low and high-mass star-forming cores, and a further mechanism for the formation was proposed that involves the dimerization of the formyl radical, HCO.
Abstract: Glycolaldehyde, the simplest monosaccharide sugar, has recently been detected in low- and high-mass star-forming cores. Following our previous investigation into glycolaldehyde formation, we now consider a further mechanism for the formation of glycolaldehyde that involves the dimerization of the formyl radical, HCO. Quantum mechanical investigation of the HCO dimerization process upon an ice surface is predicted to be barrierless and therefore fast. In an astrophysical context, we show that this mechanism can be very efficient in star-forming cores. It is limited by the availability of the formyl radical, but models suggest that only very small amounts of CO are required to be converted to HCO to meet the observational constraints.

68 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate the formation of glycolaldehyde at 10 K to determine whether it can form efficiently under typical dense core conditions using an astrochemical model, finding that a gas-phase formation route is unlikely.
Abstract: Glycolaldehyde is a simple monosaccharide sugar linked to prebiotic chemistry. Recently, it was detected in a molecular core in the star-forming region G31.41+0.31 at a reasonably high abundance. We investigate the formation of glycolaldehyde at 10 K to determine whether it can form efficiently under typical dense core conditions. Using an astrochemical model, we test five different reaction mechanisms that have been proposed in the astrophysical literature, finding that a gas-phase formation route is unlikely. Of the grain-surface formation routes, only two are efficient enough at very low temperatures to produce sufficient glycolaldehyde to match the observational estimates, with the mechanism culminating in CH3OH + HCO being favored. However, when we consider the feasibility of these mechanisms from a reaction chemistry perspective, the second grain-surface route looks more promising, H3CO + HCO.

67 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the vibrational contribution including anharmonicity and temperature dependence of the mixing enthalpy have a decisive impact on the calculated phase diagram of a Ti_{1-x}Al_{x}N alloy, lowering the maximum temperature for the miscibility gap from 6560 to 2860 K.
Abstract: We develop a method to accurately and efficiently determine the vibrational free energy as a function of temperature and volume for substitutional alloys from first principles. Taking Ti_(1−x)Al_xN alloy as a model system, we calculate the isostructural phase diagram by finding the global minimum of the free energy corresponding to the true equilibrium state of the system. We demonstrate that the vibrational contribution including anharmonicity and temperature dependence of the mixing enthalpy have a decisive impact on the calculated phase diagram of a Ti_(1−x)Al_xN alloy, lowering the maximum temperature for the miscibility gap from 6560 to 2860 K. Our local chemical composition measurements on thermally aged Ti_(0.5)Al_(0.5)N alloys agree with the calculated phase diagram.

65 citations

Journal ArticleDOI
TL;DR: It is found that the most stable arrangement of water molecules in cubic ice is isoenergetic with that of the proton ordered form of hexagonal ice (known as ice XI), indicating a potential new polytype of ice XI as XIc and a possible route for preparing ice XIc.
Abstract: Ordinary water ice forms under ambient conditions and has two polytypes, hexagonal ice (Ih) and cubic ice (Ic). From a careful comparison of proton ordering arrangements in Ih and Ic using periodic density functional theory (DFT) and diffusion Monte Carlo (DMC) approaches, we find that the most stable arrangement of water molecules in cubic ice is isoenergetic with that of the proton ordered form of hexagonal ice (known as ice XI). We denote this potential new polytype of ice XI as XIc and discuss a possible route for preparing ice XIc.

65 citations


Cited by
More filters
28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Book
01 Jan 1996
TL;DR: A review of the collected works of John Tate can be found in this paper, where the authors present two volumes of the Abel Prize for number theory, Parts I, II, edited by Barry Mazur and Jean-Pierre Serre.
Abstract: This is a review of Collected Works of John Tate. Parts I, II, edited by Barry Mazur and Jean-Pierre Serre. American Mathematical Society, Providence, Rhode Island, 2016. For several decades it has been clear to the friends and colleagues of John Tate that a “Collected Works” was merited. The award of the Abel Prize to Tate in 2010 added impetus, and finally, in Tate’s ninety-second year we have these two magnificent volumes, edited by Barry Mazur and Jean-Pierre Serre. Beyond Tate’s published articles, they include five unpublished articles and a selection of his letters, most accompanied by Tate’s comments, and a collection of photographs of Tate. For an overview of Tate’s work, the editors refer the reader to [4]. Before discussing the volumes, I describe some of Tate’s work. 1. Hecke L-series and Tate’s thesis Like many budding number theorists, Tate’s favorite theorem when young was Gauss’s law of quadratic reciprocity. When he arrived at Princeton as a graduate student in 1946, he was fortunate to find there the person, Emil Artin, who had discovered the most general reciprocity law, so solving Hilbert’s ninth problem. By 1920, the German school of algebraic number theorists (Hilbert, Weber, . . .) together with its brilliant student Takagi had succeeded in classifying the abelian extensions of a number field K: to each group I of ideal classes in K, there is attached an extension L of K (the class field of I); the group I determines the arithmetic of the extension L/K, and the Galois group of L/K is isomorphic to I. Artin’s contribution was to prove (in 1927) that there is a natural isomorphism from I to the Galois group of L/K. When the base field contains an appropriate root of 1, Artin’s isomorphism gives a reciprocity law, and all possible reciprocity laws arise this way. In the 1930s, Chevalley reworked abelian class field theory. In particular, he replaced “ideals” with his “idèles” which greatly clarified the relation between the local and global aspects of the theory. For his thesis, Artin suggested that Tate do the same for Hecke L-series. When Hecke proved that the abelian L-functions of number fields (generalizations of Dirichlet’s L-functions) have an analytic continuation throughout the plane with a functional equation of the expected type, he saw that his methods applied even to a new kind of L-function, now named after him. Once Tate had developed his harmonic analysis of local fields and of the idèle group, he was able prove analytic continuation and functional equations for all the relevant L-series without Hecke’s complicated theta-formulas. Received by the editors September 5, 2016. 2010 Mathematics Subject Classification. Primary 01A75, 11-06, 14-06. c ©2017 American Mathematical Society

2,014 citations

Journal ArticleDOI
01 Jun 2021
TL;DR: Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems are discussed.
Abstract: Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Here, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems. The rapidly developing field of physics-informed learning integrates data and mathematical models seamlessly, enabling accurate inference of realistic and high-dimensional multiphysics problems. This Review discusses the methodology and provides diverse examples and an outlook for further developments.

1,114 citations

Posted Content
TL;DR: In this article, a coarse-grained model of water (mW) was developed, which is essentially an atom with tetrahedrality intermediate between carbon and silicon, and mimics the hydrogen-bonded structure of water through the introduction of a nonbond angular dependent term.
Abstract: Water and silicon are chemically dissimilar substances with common physical properties. Their liquids display a temperature of maximum density, increased diffusivity on compression, they form tetrahedral crystals and tetrahedral amorphous phases. The common feature to water, silicon and carbon is the formation of tetrahedrally coordinated units. We exploit these similarities to develop a coarse-grained model of water (mW) that is essentially an atom with tetrahedrality intermediate between carbon and silicon. mW mimics the hydrogen-bonded structure of water through the introduction of a nonbond angular dependent term that encourages tetrahedral configurations. The model departs from the prevailing paradigm in water modeling: the use of long-ranged forces (electrostatics) to produce short-ranged (hydrogen-bonded) structure. mW has only short-range interactions yet it reproduces the energetics, density and structure of liquid water, its anomalies and phase transitions with comparable or better accuracy than the most popular atomistic models of water, at less than 1% of the computational cost. We conclude that it is not the nature of the interactions but the connectivity of the molecules that determines the structural and thermodynamic behavior of water. The speedup in computing time provided by mW makes it particularly useful for the study of slow processes in deeply supercooled water, the mechanism of ice nucleation, wetting-drying transitions, and as a realistic water model for coarse-grained simulations of biomolecules and complex materials.

562 citations

Journal ArticleDOI
TL;DR: In this article, a review of Kohn-Sham density functional theory for describing aqueous systems of all kinds, including those important in chemistry, surface science, biology, and the earth sciences, is presented.
Abstract: Kohn-Sham density functional theory (DFT) has become established as an indispensable tool for investigating aqueous systems of all kinds, including those important in chemistry, surface science, biology, and the earth sciences. Nevertheless, many widely used approximations for the exchange-correlation (XC) functional describe the properties of pure water systems with an accuracy that is not fully satisfactory. The explicit inclusion of dispersion interactions generally improves the description, but there remain large disagreements between the predictions of different dispersion-inclusive methods. We present here a review of DFT work on water clusters, ice structures, and liquid water, with the aim of elucidating how the strengths and weaknesses of different XC approximations manifest themselves across this variety of water systems. Our review highlights the crucial role of dispersion in describing the delicate balance between compact and extended structures of many different water systems, including the liquid. By referring to a wide range of published work, we argue that the correct description of exchange-overlap interactions is also extremely important, so that the choice of semi-local or hybrid functional employed in dispersion-inclusive methods is crucial. The origins and consequences of beyond-2-body errors of approximate XC functionals are noted, and we also discuss the substantial differences between different representations of dispersion. We propose a simple numerical scoring system that rates the performance of different XC functionals in describing water systems, and we suggest possible future developments.

526 citations