Kalavathy Rajan

Prof. Dr. Kalavathy Rajan

Center for Renewable Carbon, The University of Tennessee, Knoxville, TN 37996, USA
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Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 September 2018 in ACS Applied Materials & Interfaces
Verified
Journal Article
Closed access
JSJordan T. Sutton
Jordan T. Sutton
Kalavathy RajanKalavathy Rajan
Kalavathy Rajan
David Paul HarperDavid Paul Harper
David Paul Harper
SCStephen C. Chmely
Stephen C. Chmely


31 March 2020 in Bioresources and Bioprocessing
Verified
Journal Article
Open access
PDF file
GKGurshagan Kandhola
Gurshagan Kandhola
ADAngele Djioleu
Angele Djioleu
Kalavathy RajanKalavathy Rajan
Kalavathy Rajan
NLNicole Labbé
Nicole Labbé
JSJoshua Sakon
Joshua Sakon
JKJin-Woo Kim
Jin-Woo Kim
This study aims to optimize strong acid hydrolysis-based production of cellulose nanocrystals (CNCs) and cellulose nanofibers (CNFs) from pre-extracted and fully bleached kraft pulp of loblolly pinewood, the most abundant and commercially significant softwood species in southeastern United States. The effect of four parameters, including acid concentration, temperature, duration and pulp particle size, on the yield and properties of CNCs was investigated using the central composite design (CCD) of response surface methodology (RSM) for process optimization. While CNC yield was significantly affected by acid concentration and hydrolysis temperature and was adequately explained by an empirical model, none of the characteristic properties of CNCs, including crystallinity index, surface charge and particle size, displayed any strong correlation to the process parameters within the experimental ranges tested. At different hydrolysis severities, we not only analyzed the waste streams to determine the extent of holocellulose degradation, but also evaluated the properties of leftover partially hydrolyzed pulp, called cellulosic solid residues (CSR), to gauge its potential for CNF production via mechanical fibrillation. Conditions that maximized CNC yields (60% w/w) were 60% acid concentration, 58 °C, 60 min and 40 mesh particle size. Twenty percent (w/w) of the pulp was degraded under these conditions. On the other hand, conditions that maximized CSR yields (60% w/w) were 54% acid, 45 °C, 90 min and 20 mesh particle size, which also produced 15% CNCs, caused minimal pulp degradation (< 5%) and imparted sufficient surface charge such that CSR was easily microfluidized into CNFs. Therefore, the strong acid hydrolysis process could be tuned to maximize the production of cellulose nanocrystals and nanofibers and obtain two products with different properties and applications through the process optimization.

8 November 2016 in Critical Reviews in Microbiology
Verified
Journal Article
Closed access
Kalavathy RajanKalavathy Rajan
Kalavathy Rajan
ZSZhaohao Shi
Zhaohao Shi
SRSteven C. Ricke
Steven C. Ricke
One of the leading causes of foodborne illness in poultry products is Salmonella enterica. Salmonella hazards in poultry may be estimated and possible control methods modeled and evaluated through the use of quantitative microbiological risk assessment (QMRA) models and tools. From farm to table, there are many possible routes of Salmonella dissemination and contamination in poultry. From the time chicks are hatched through growth, transportation, processing, storage, preparation, and finally consumption, the product could be contaminated through exposure to different materials and sources. Examination of each step of the process is necessary as well as an examination of the overall picture to create effective countermeasures against contamination and prevent disease. QMRA simulation models can use either point estimates or probability distributions to examine variables such as Salmonella concentrations at retail or at any given point of processing to gain insight on the chance of illness due to Salmonella ingestion. For modeling Salmonella risk in poultry, it is important to look at variables such as Salmonella transfer and cross contamination during processing. QMRA results may be useful for the identification and control of critical sources of Salmonella contamination.

4 April 2018 in Biomacromolecules
Verified
Journal Article
Closed access
Kalavathy RajanKalavathy Rajan
Kalavathy Rajan
JMJeffrey K. Mann
Jeffrey K. Mann
EEEldon English
Eldon English
David Paul HarperDavid Paul Harper
David Paul Harper
DCDanielle Julie Carrier
Danielle Julie Carrier
SCStephen C. Chmely
Stephen C. Chmely

1 May 2015 in Biomass and Bioenergy
Verified
Journal Article
Closed access
SMSaritha Mohanram
Saritha Mohanram
Kalavathy RajanKalavathy Rajan
Kalavathy Rajan
DCDanielle Julie Carrier
Danielle Julie Carrier
LNLata Nain
Lata Nain
AAAnju Arora
Anju Arora