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Institution

Indian Institute of Science

EducationBengaluru, India
About: Indian Institute of Science is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Thin film & Population. The organization has 30960 authors who have published 62497 publications receiving 1257765 citations. The organization is also known as: IISC & IISc.


Papers
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Journal ArticleDOI
TL;DR: In this article, a definition for the term ''halogen bond'' is proposed, which designates a specific subset of the inter-and intramolecular interactions involving a halogen atom in a molecular entity.
Abstract: This recommendation proposes a definition for the term ``halogen bond'', which designates a specific subset of the inter- and intramolecular interactions involving a halogen atom in a molecular entity.

1,386 citations

Journal ArticleDOI
TL;DR: In this paper, a novel definition for the hydrogen bond is proposed, which takes into account the theoretical and experimental knowledge acquired over the past century, and six criteria are listed that could be used as evidence for the presence of a hydrogen bond.
Abstract: A novel definition for the hydrogen bond is recommended here. It takes into account the theoretical and experimental knowledge acquired over the past century. This def- inition insists on some evidence. Six criteria are listed that could be used as evidence for the presence of a hydrogen bond.

1,367 citations

Journal ArticleDOI
TL;DR: This work introduces a mathematical model of hybrid systems as interacting collections of dynamical systems, evolving on continuous-variable state spaces and subject to continuous controls and discrete transitions, and develops a theory for synthesizing hybrid controllers for hybrid plants in all optimal control framework.
Abstract: We propose a very general framework that systematizes the notion of a hybrid system, combining differential equations and automata, governed by a hybrid controller that issues continuous-variable commands and makes logical decisions. We first identify the phenomena that arise in real-world hybrid systems. Then, we introduce a mathematical model of hybrid systems as interacting collections of dynamical systems, evolving on continuous-variable state spaces and subject to continuous controls and discrete transitions. The model captures the identified phenomena, subsumes previous models, yet retains enough structure to pose and solve meaningful control problems. We develop a theory for synthesizing hybrid controllers for hybrid plants in all optimal control framework. In particular, we demonstrate the existence of optimal (relaxed) and near-optimal (precise) controls and derive "generalized quasi-variational inequalities" that the associated value function satisfies. We summarize algorithms for solving these inequalities based on a generalized Bellman equation, impulse control, and linear programming.

1,363 citations

Journal ArticleDOI
01 Jun 1999
TL;DR: A novel hybrid genetic algorithm that finds a globally optimal partition of a given data into a specified number of clusters using a classical gradient descent algorithm used in clustering, viz.
Abstract: In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA's used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent algorithm used in clustering, viz. K-means algorithm. Hence, the name genetic K-means algorithm (GKA). We define K-means operator, one-step of K-means algorithm, and use it in GKA as a search operator instead of crossover. We also define a biased mutation operator specific to clustering called distance-based-mutation. Using finite Markov chain theory, we prove that the GKA converges to the global optimum. It is observed in the simulations that GKA converges to the best known optimum corresponding to the given data in concurrence with the convergence result. It is also observed that GKA searches faster than some of the other evolutionary algorithms used for clustering.

1,326 citations

Journal ArticleDOI
TL;DR: Graphene-on-MoS2 binary heterostructures display remarkable dual optoelectronic functionality, including highly sensitive photodetection and gate-tunable persistent photoconductivity, and may lead to new graphene-based optoeLECTronic devices that are naturally scalable for large-area applications at room temperature.
Abstract: Combining the electronic properties of graphene(1,2) and molybdenum disulphide (MoS2)(3-6) in hybrid heterostructures offers the possibility to create devices with various functionalities. Electronic logic and memory devices have already been constructed from graphene-MoS2 hybrids(7,8), but they do not make use of the photosensitivity of MoS2, which arises from its optical-range bandgap(9). Here, we demonstrate that graphene-on-MoS2 binary heterostructures display remarkable dual optoelectronic functionality, including highly sensitive photodetection and gate-tunable persistent photoconductivity. The responsivity of the hybrids was found to be nearly 1 x 10(10) A W-1 at 130 K and 5 x 10(8) A W-1 at room temperature, making them the most sensitive graphene-based photodetectors. When subjected to time-dependent photoillumination, the hybrids could also function as a rewritable optoelectronic switch or memory, where the persistent state shows almost no relaxation or decay within experimental timescales, indicating near-perfect charge retention. These effects can be quantitatively explained by gate-tunable charge exchange between the graphene and MoS2 layers, and may lead to new graphene-based optoelectronic devices that are naturally scalable for large-area applications at room temperature.

1,275 citations


Authors

Showing all 31242 results

NameH-indexPapersCitations
Alan J. Heeger171913147492
William A. Goddard1511653123322
Rajesh Kumar1494439140830
Kaushik De1391625102058
Tariq Aziz138164696586
Jean-Marie Tarascon136853137673
Kajari Mazumdar134129594253
C. N. R. Rao133164686718
Gobinda Majumder133152387732
Ramesh Narayan12966163628
Seema Sharma129156585446
Jyothsna Rani Komaragiri129109782258
Alex K.-Y. Jen12892161811
Sushil Chauhan128112978835
Somnath Choudhury128126480929
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023126
2022528
20214,147
20204,075
20193,669
20183,552