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Institution

Indian Institute of Technology Mandi

EducationMandi, India
About: Indian Institute of Technology Mandi is a education organization based out in Mandi, India. It is known for research contribution in the topics: Photocatalysis & Piezoelectricity. The organization has 1022 authors who have published 2842 publications receiving 27410 citations.


Papers
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Journal ArticleDOI
TL;DR: A critical review that encompasses the fundamentals and state-of-the-art knowledge of barium titanate-based piezoelectrics is presented in this paper, where a detailed compilation of their functional and mechanical properties is provided.
Abstract: We present a critical review that encompasses the fundamentals and state-of-the-art knowledge of barium titanate-based piezoelectrics. First, the essential crystallography, thermodynamic relations, and concepts necessary to understand piezoelectricity and ferroelectricity in barium titanate are discussed. Strategies to optimize piezoelectric properties through microstructure control and chemical modification are also introduced. Thereafter, we systematically review the synthesis, microstructure, and phase diagrams of barium titanate-based piezoelectrics and provide a detailed compilation of their functional and mechanical properties. The most salient materials treated include the (Ba,Ca)(Zr,Ti)O3, (Ba,Ca)(Sn,Ti)O3, and (Ba,Ca)(Hf,Ti)O3 solid solution systems. The technological relevance of barium titanate-based piezoelectrics is also discussed and some potential market indicators are outlined. Finally, perspectives on productive lines of future research and promising areas for the applications of these ma...

697 citations

Journal ArticleDOI
TL;DR: In this paper, the pyroelectric effect and potential thermal and electric field cycles for energy harvesting are explored, as well as pyro-electric architectures and systems that can be employed to improve device performance.
Abstract: This review covers energy harvesting technologies associated with pyroelectric materials and systems. Such materials have the potential to generate electrical power from thermal fluctuations and is a less well explored form of thermal energy harvesting than thermoelectric systems. The pyroelectric effect and potential thermal and electric field cycles for energy harvesting are explored. Materials of interest are discussed and pyroelectric architectures and systems that can be employed to improve device performance, such as frequency and power level, are described. In addition to the solid materials employed, the appropriate pyroelectric harvesting circuits to condition and store the electrical power are discussed.

589 citations

Journal ArticleDOI
TL;DR: An extensive review related to the structural response of the functionally graded materials (FGMs) and structures have been presented in this article, where the emphasis has been made here, to present the structural characteristics of FGMs plates/shells under thermo-electro-mechanical loadings under various boundary and environmental conditions.

336 citations

Journal ArticleDOI
TL;DR: The use of solar energy to catalyze the photo-driven processes has attracted tremendous attention from the scientific community because of its great potential to address energy and environmental is....
Abstract: The use of solar energy to catalyze the photo-driven processes has attracted tremendous attention from the scientific community because of its great potential to address energy and environmental is...

312 citations

Proceedings Article
15 Feb 2018
TL;DR: Empirical evaluation on three different applications establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbations methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.
Abstract: We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other's objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.

276 citations


Authors

Showing all 1080 results

NameH-indexPapersCitations
Rajesh Kumar1494439140830
Subrata Ghosh7884132147
Pradeep Kumar61139019257
Himanshu Pathak5625911203
Ajay Kumar5380912181
Ralph Skomski4843211118
Sumant Nigam47947063
M. Rajeevan461649115
Ashish Tiwari381815745
Raghavan Kumar382975527
Venkata Krishnan351513668
Neha Garg311495499
Rahul Vaish313024749
Satish C. Jain291212632
Prem Felix Siril26621811
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202331
202292
2021507
2020526
2019386
2018396