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Items: 1 to 20 of 39

1.

The effect of strain on effective Duffing nonlinearity in the CVD-MoS2 resonator.

Samanta C, Arora N, V KK, Raghavan S, Naik AK.

Nanoscale. 2019 Apr 25;11(17):8394-8401. doi: 10.1039/c8nr10452b.

PMID:
30984929
2.

Reversible defect engineering in graphene grain boundaries.

Balasubramanian K, Biswas T, Ghosh P, Suran S, Mishra A, Mishra R, Sachan R, Jain M, Varma M, Pratap R, Raghavan S.

Nat Commun. 2019 Mar 6;10(1):1090. doi: 10.1038/s41467-019-09000-8.

3.

Ground and satellite based observation datasets for the Lower Mekong River Basin.

Mohammed IN, Bolten JD, Srinivasan R, Meechaiya C, Spruce JP, Lakshmi V.

Data Brief. 2018 Nov 14;21:2020-2027. doi: 10.1016/j.dib.2018.11.038. eCollection 2018 Dec.

4.

Satellite observations and modeling to understand the Lower Mekong River basin streamflow variability.

Mohammed IN, Bolten JD, Srinivasan R, Lakshmi V.

J Hydrol (Amst). 2018 Sep;564:559-573. doi: 10.1016/j.jhydrol.2018.07.030. Epub 2018 Jul 24.

PMID:
30100623
5.

Improved Hydrological Decision Support System for the Lower Mekong River Basin Using Satellite-Based Earth Observations.

Mohammed IN, Bolten JD, Srinivasan R, Lakshmi V.

Remote Sens (Basel). 2018 Jun;10(6):885. doi: 10.3390/rs10060885. Epub 2018 Jun 6.

6.

Positive Influence of Behavior Change Communication on Knowledge, Attitudes, and Practices for Visceral Leishmaniasis/Kala-azar in India.

Srinivasan R, Ahmad T, Raghavan V, Kaushik M, Pathak R.

Glob Health Sci Pract. 2018 Mar 30;6(1):192-209. doi: 10.9745/GHSP-D-17-00087. Print 2018 Mar 21.

7.

Integrating multimedia models to assess nitrogen losses from the Mississippi River basin to the Gulf of Mexico.

Yuan Y, Wang R, Cooter E, Ran L, Daggupati P, Yang D, Srinivasan R, Jalowska A.

Biogeosciences. 2018;15:7059-7076. doi: 10.5194/bg-15-7059-2018.

8.

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds.

Winchell MF, Peranginangin N, Srinivasan R, Chen W.

Integr Environ Assess Manag. 2018 May;14(3):358-368. doi: 10.1002/ieam.2014. Epub 2018 Jan 12.

PMID:
29193759
9.

Thickness Dependent Parasitic Channel Formation at AlN/Si Interfaces.

Chandrasekar H, Bhat KN, Rangarajan M, Raghavan S, Bhat N.

Sci Rep. 2017 Nov 16;7(1):15749. doi: 10.1038/s41598-017-16114-w.

10.

Evaluation of bioenergy crop growth and the impacts of bioenergy crops on streamflow, tile drain flow and nutrient losses in an extensively tile-drained watershed using SWAT.

Guo T, Cibin R, Chaubey I, Gitau M, Arnold JG, Srinivasan R, Kiniry JR, Engel BA.

Sci Total Environ. 2018 Feb 1;613-614:724-735. doi: 10.1016/j.scitotenv.2017.09.148. Epub 2017 Sep 20.

PMID:
28938215
11.

Evaluation of new farming technologies in Ethiopia using the Integrated Decision Support System (IDSS).

Clarke N, Bizimana JC, Dile Y, Worqlul A, Osorio J, Herbst B, Richardson JW, Srinivasan R, Gerik TJ, Williams J, Jones CA, Jeong J.

Agric Water Manag. 2017 Jan 31;180(Pt B):267-279. doi: 10.1016/j.agwat.2016.07.023.

12.

Spatial and temporal patterns of precipitation and stream flow variations in Tigris-Euphrates river basin.

Daggupati P, Srinivasan R, Ahmadi M, Verma D.

Environ Monit Assess. 2017 Jan;189(2):50. doi: 10.1007/s10661-016-5752-y. Epub 2017 Jan 5.

PMID:
28058613
13.

Reconstructing the historical water regime of the contributing basins to the Hawizeh marsh: Implications of water control structures.

Daggupati P, Srinivasan R, Dile YT, Verma D.

Sci Total Environ. 2017 Feb 15;580:832-845. doi: 10.1016/j.scitotenv.2016.12.029. Epub 2016 Dec 21.

PMID:
28012653
14.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

Yen H, White MJ, Arnold JG, Keitzer SC, Johnson MV, Atwood JD, Daggupati P, Herbert ME, Sowa SP, Ludsin SA, Robertson DM, Srinivasan R, Rewa CA.

Sci Total Environ. 2016 Nov 1;569-570:1265-1281. doi: 10.1016/j.scitotenv.2016.06.202. Epub 2016 Jul 5.

PMID:
27387796
15.

Million-Fold Decrease in Polymer Moisture Permeability by a Graphene Monolayer.

Seethamraju S, Kumar S, B KB, Madras G, Raghavan S, Ramamurthy PC.

ACS Nano. 2016 Jul 26;10(7):6501-9. doi: 10.1021/acsnano.6b02588. Epub 2016 Jul 13.

PMID:
27314156
16.

Bright-field Nanoscopy: Visualizing Nano-structures with Localized Optical Contrast Using a Conventional Microscope.

Suran S, Bharadwaj K, Raghavan S, Varma MM.

Sci Rep. 2016 Apr 26;6:25011. doi: 10.1038/srep25011.

17.

Making consistent contacts to graphene: effect of architecture and growth induced defects.

Krishna Bharadwaj B, Nath D, Pratap R, Raghavan S.

Nanotechnology. 2016 May 20;27(20):205705. doi: 10.1088/0957-4484/27/20/205705. Epub 2016 Apr 12.

PMID:
27070858
18.

Magnitude and Origin of Electrical Noise at Individual Grain Boundaries in Graphene.

Kochat V, Tiwary CS, Biswas T, Ramalingam G, Hsieh K, Chattopadhyay K, Raghavan S, Jain M, Ghosh A.

Nano Lett. 2016 Jan 13;16(1):562-7. doi: 10.1021/acs.nanolett.5b04234. Epub 2015 Dec 7.

PMID:
26632989
19.

Assessing the implications of water harvesting intensification on upstream-downstream ecosystem services: A case study in the Lake Tana basin.

Dile YT, Karlberg L, Daggupati P, Srinivasan R, Wiberg D, Rockström J.

Sci Total Environ. 2016 Jan 15;542(Pt A):22-35. doi: 10.1016/j.scitotenv.2015.10.065. Epub 2015 Oct 27.

PMID:
26519564
20.

A predictive approach to CVD of crystalline layers of TMDs: the case of MoS2.

Kranthi Kumar V, Dhar S, Choudhury TH, Shivashankar SA, Raghavan S.

Nanoscale. 2015 May 7;7(17):7802-10. doi: 10.1039/c4nr07080a.

PMID:
25849114

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