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To describe the natural course of procalcitonin (PCT) in patients with coronavirus disease 2019 (COVID-19) and the correlation between PCT and antimicrobial prescribing to provide insight into best practices for PCT data utilization in antimicrobial stewardship in this population.
Inpatients aged ≥18 years hospitalized March 1, 2020, through October 31, 2021, who were positive for severe acute respiratory coronavirus virus 2 (SARS-CoV-2), with ≥1 PCT measurement. Exclusion criteria included antibiotics for nonpulmonary bacterial infection on admission, treatment with azithromycin only for chronic obstructive pulmonary disease (COPD) exacerbation, and pre-existing diagnosis of cystic fibrosis with positive respiratory cultures.
A structured query was used to extract data. For patients started on antibiotics, bacterial pneumonia (bPNA) was determined through chart review. Multivariable models were used to assess associations of PCT level and bPNA with antimicrobial use.
Of 793 patients, 224 (28.2%) were initiated on antibiotics: 33 (14.7%) had proven or probable bPNA, 125 (55.8%) had possible bPNA, and 66 (29.5%) had no bPNA. Patients had a mean of 4.1 (SD, ±5.2) PCT measurements if receiving antibiotics versus a mean of 2.0 (SD, ±2.6) if not. Initial PCT level was highest for those with proven/probable bPNA and was associated with antibiotic initiation (odds ratio 95% confidence interval [CI], 1.17–1.30). Initial PCT (rate ratio [RR] 95% CI, 1.01–1.08), change in PCT over time (RR 95% CI, 1.01–1.05), and bPNA group (RR 95% CI, 1.23–1.84) were associated with antibiotic duration.
PCT trends are associated with the decision to initiate antibiotics and duration of treatment, independent of bPNA status and comorbidities. Prospective studies are needed to determine whether PCT level can be used to safely make decisions regarding antibiotic treatment for COVID-19.
Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.
Background:Clostridioides difficile infection (CDI) is a major source of morbidity and mortality. Even after recovery, recurrent CDI (rCDI) occurs frequently, and concomitant antibiotic use for treatment of a concurrent non–C. difficile infection is a major risk factor. Treatment with fidaxomicin versus vancomycin is associated with similar rate of cure and lower recurrence risk. However, the comparative efficacy of these 2 agents remains unclear in those receiving concomitant antibiotics. Methods: We conducted a randomized, controlled, open-label trial at the University of Michigan and St. Joseph Mercy hospitals in Ann Arbor, Michigan. Patients provided written informed consent at enrollment. We included all hospitalized patients aged ≥18 years with a positive test for toxigenic C. difficile, >3 unformed stools per 24 hours, and ≥1 qualifying concomitant antibiotic with a planned treatment of an infection for ≥5 days after enrollment. We excluded patients with complicated CDI, allergy to vancomycin–fidaxomicin, planned adjunctive CDI treatments, CDI treatment for >24 hours prior to enrollment, concomitant laxative use, current or planned colostomy or ileostomy, and/or planned long-term (>12 weeks) concomitant antibiotic use. Clinical cure was defined as resolution of diarrhea for 2 consecutive days maintained until the end of therapy and for 2 days afterward. rCDI was defined as recurrent diarrhea with positive testing within 30 days of initial treatment. Patients were randomized (stratified by ICU status) to fidaxomicin 200 mg twice daily or vancomycin 125 mg orally 4 times daily for 10 days. If concomitant antibiotic treatment continued >10 days, the study drug continued until the concomitant antibiotic ended. Bivariable statistics included t tests and χ2 tests. Results: After screening 5,101 patients for eligibility (May 2017–May 2021), 144 were included and randomized (Fig. 1). Study characteristics and outcomes are noted in Table 1. Baseline characteristics were similar between groups. Most patients were aged <65 years, were on a proton-pump inhibitor (PPI), and were not in the ICU. The mean duration of concomitant antibiotic was 18.4 days. In the intention-to-treat population, clinical cure (73% vs 62.9%; P =.195), and rCDI (3.3% vs 4.0%; P >.99) were similar for fidaxomicin and vancomycin, respectively. Conclusions: In this study of patients with CDI receiving a concomitant antibiotic, a numerically higher proportion were cured with fidaxomicin versus vancomycin, but this result did not reach statistical significance. Overall recurrence was lower than anticipated in both arms compared to previous studies in which duration of CDI treatment was not extended during concomitant antibiotic treatment. Future studies are needed to ascertain whether clinical cure is higher with fidaxomicin than vancomycin during concomitant antibiotic exposure, and whether extending the duration of CDI treatment reduces recurrence.
Vechur cow is an indigenous cattle breed of Kerala listed as a critical breed by FAO. This research communication is related to the hypothesis that the changes occurring in microbiological quality parameters of Vechur cow milk dahi (VCMD) during storage will be superior to other milk and reflective of the traditional concepts of therapeutic properties attributed to Vechur milk. Microbiological quality of the VCMD stored at room (30 ± 1°C) and refrigerated (4 ± 1°C) temperatures in terms of total viable, coliform, yeast and mold and lactococcal counts is reported in this study, together with titratable acidity and pH. Results are compared with cross-bred cow milk dahi (CCMD) as control. On refrigerated storage, despite the comparable initial microbiological quality, VCMD exhibited significantly lower total viable, lactic acid bacteria, yeast and mold counts than CCMD, from the fifth day onwards for the first two parameters and the tenth day onwards for the last parameter. VCMD exhibited significantly higher pH values than CCMD from the fifth day onwards whereas the titratable acidity was significantly lower from the tenth day onwards. Though this study does not delineate the factors contributing towards the lower microbial population observed in VCMD, it provides an impetus to further researches for scientifically validating its traditionally-reported medicinal properties.
Background: Patient colonization and shedding of vancomycin-resistant enterococci (VRE) is a major source of environmental contamination leading to VRE transmission in nursing homes. We hypothesize that we can inform mitigation strategies by identifying patient clinical and microbiota features associated with environmental contamination with VRE. Methods: During a 6-month period of active surveillance in 6 Michigan nursing homes, 245 patients (with 806 follow-up visits) were enrolled. Patient clinical data and swabs for VRE were collected from multiple body sites and high-touch environmental surfaces. In total, 316 perirectal swabs were collected from 137 patients for gut microbiota analysis and community status type (CST) assignment based on taxonomic composition. The associations between VRE colonization pattern, gut microbial CST, and patient factors were examined using multivariable generalized estimating equations, adjusting for patient-and facility-level clustering. We used VRE colonization patterns to group study visits: “uncolonized” (patient−/environment−); “environment-only” (patient−/environment+); “patient-only” (patient+/environment−); “both” (patient+/environment+). Results: Across all study visits, VRE colonization on patient hand and groin/perirectal area was positively correlated with VRE contamination of high-touch environmental surfaces, suggesting direct transfer of VRE between patient and environment via patient hands (Figure 1A). We next set out to identify patient factors associated with patient colonization and environmental contamination. At baseline, while patients in the “both” group had anticipated risk factors such as longer prior hospitalization and more frequent broad-spectrum antibiotic use, they were unexpectedly younger than “uncolonized” patients and had similar functional status. This last feature contrasted with the “patient-only” group, characterized by higher urinary catheter use and higher functional dependence, suggestive of lower functional dependence facilitating patient contamination of their environment. No clinical features distinguished “uncolonized” and “environment-only” patients (Table 1). Lastly, in multivariable analyses, we determined the contribution of patient functional status and gut microbiota features to environmental contamination. Low-diversity CST, characterized by reduced anaerobic taxa, was weakly associated with “patient-only” and significantly associated with “both.” Notably, high functional dependence was significantly associated with “environment-only” and “patient-only” but not “both,” indicating high-functioning patients with disrupted gut microbiota as drivers of environmental contamination (Figure 1B). Conclusions: Our findings suggest that antimicrobial exposure disrupts patient gut microbiota, a significant mediator of colonization dynamics between patients and their environment, and that high-functioning patients may be more likely to spread VRE between their body sites and high-touch environmental surfaces (Figure 2). These findings highlight both antibiotic stewardship and patient hand hygiene as important targets for interrupting transmission mediated by environmental contamination.
A diverse set of 107 rice genotypes was evaluated for yield, shattering and dormancy traits. Analysis of variance revealed sizable variation while skewness and kurtosis values indicated near-normal distribution for most of the traits, thus quantitative nature controlled by many genes. A highly significant deviation from a normal distribution for dormancy and shattering % indicated their qualitative nature of inheritance. Four promising genotypes ‘IRGC1723’ (early with 65 days to flowering), ‘IRGC 11108’ and ‘RNR 15459’ (high grain number – 358 and low average shattering – <5%), ‘RNR 11718’ (high single plant yield – 56.73 g, low average shattering – <5% and dormancy period – 21 days) are identified. A significant positive correlation between shattering and dormancy confirms inter-relationship among domestication-related characteristics. The principal component analysis revealed the contribution of four PCs to maximum variability and hierarchical clustering grouped the genotypes into 18 divergent clusters. Five cultivars (Karimnagar Samba, Sheetal, PR 121, Pranahitha and Jagitial Samba) with a combination of low shattering ability (3.35–5.7%) and considerable dormancy period (13–20 days) falling in the same cluster can be used as donors for the improvement of rice genotypes with low shattering ability and incorporating a considerable period of dormancy so as to avoid pre-harvest sprouting due to delayed harvesting. Further, they can be crossed with ‘Pratyumna’ having less than 1 week dormancy period, a genotype of the cluster XVII with which they have a maximum genetic divergence of 51.4 and may serve as parents in the development of mapping populations for the identification of QTLs/genes for shattering and dormancy traits.
ABSTRACT IMPACT: Our goal is to identify bacterial biomarkers of adverse Clostridioides difficile infection outcomes OBJECTIVES/GOALS: We characterized microbiota features of Clostridioides difficile infections (CDIs) and will investigate the association between bacterial taxa and adverse outcomes, which includes severe and recurrent CDIs. METHODS/STUDY POPULATION: 1,517 stool samples were collected from patients diagnosed with a CDI at the University of Michigan along with 1,516 unformed and 910 formed stool control samples. We characterized the microbiota of the 3,943 stool samples by sequencing the V4 region of the 16S rRNA gene and used the Dirichlet Multinomial Mixtures method to cluster samples into community types. Severe CDI cases were defined using the Infectious Diseases Society of America criteria and recurrent CDIs were defined as CDIs that occurred within 2-12 weeks of the primary CDI. We will use machine learning to examine whether specific bacterial taxa can predict severe or recurrent CDIs. We will test 5 machine learning models with 80% training and 20% testing data split. RESULTS/ANTICIPATED RESULTS: Similar to findings from a previous study with 338 samples, we found there was no difference in diversity between CDI cases and unformed controls (Inverse Simpson index, p > 0.5) and samples from the 3 groups (CDIs, unformed controls, and formed controls) clustered into 12 community types. To investigate the bacterial taxa that are important for predicting adverse CDI outcomes, we will select the best machine learning model based on performance and training time and examine how much each feature contributes to performance. We anticipate the large number of CDI cases in our cohort and robust machine learning approaches will enable us to identify more bacteria associated with adverse outcomes compared to other studies that have attempted to predict CDI recurrence with fewer CDI cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: Adverse CDI outcomes are a significant source of the morbidities, mortalities, and healthcare costs associated with CDIs. Identifying bacterial biomarkers of severe and recurrent CDIs could enhance our ability to stratify patients into risk groups and may lead to the development of more targeted therapeutics.
Background:Clostridioides difficile infection (CDI) frequently recurs after initial treatment. Predicting recurrent CDI (rCDI) early in the disease course can assist clinicians in their decision making and improve outcomes. However, predictions based on clinical criteria alone are not accurate and/or do not validate other results. Here, we tested the hypothesis that circulating and stool-derived inflammatory mediators predict rCDI. Methods: Consecutive subjects with available specimens at diagnosis were included if they tested positive for toxigenic C. difficile (+enzyme immunoassay [EIA] for glutamate dehydrogenase and toxins A/B, with reflex to PCR for the tcdB gene for discordants). Stool was thawed on ice, diluted 1:1 in PBS with protease inhibitor, centrifuged, and used immediately. A 17-plex panel of inflammatory mediators was run on a Luminex 200 machine using a custom antibody-linked bead array. Prior to analysis, all measurements were normalized and log-transformed. Stool toxin activity levels were quantified using a custom cell-culture assay. Recurrence was defined as a second episode of CDI within 100 days. Ordination characterized variation in the panel between outcomes, tested with a permutational, multivariate ANOVA. Machine learning via elastic net regression with 100 iterations of 5-fold cross validation selected the optimal model and the area under the receiver operator characteristic curve (AuROC) was computed. Sensitivity analyses excluding those that died and/or lived >100 km away were performed. Results: We included 186 subjects, with 95 women (51.1%) and average age of 55.9 years (±20). More patients were diagnosed by PCR than toxin EIA (170 vs 55, respectively). Death, rCDI, and no rCDI occurred in 32 (17.2%), 36 (19.4%), and 118 (63.4%) subjects, respectively. Ordination revealed that the serum panel was associated with rCDI (P = .007) but the stool panel was not. Serum procalcitonin, IL-8, IL-6, CCL5, and EGF were associated with recurrence. The machine-learning models using the serum panel predicted rCDI with AuROCs between 0.74 and 0.8 (Fig. 1). No stool inflammatory mediators independently predicted rCDI. However, stool IL-8 interacted with toxin activity to predict rCDI (Fig. 2). These results did not change significantly upon sensitivity analysis. Conclusions: A panel of serum inflammatory mediators predicted rCDI with up to 80% accuracy, but the stool panel alone was less successful. Incorporating toxin activity levels alongside inflammatory mediator measurements is a novel, promising approach to studying stool-derived biomarkers of rCDI. This approach revealed that stool IL-8 is a potential biomarker for rCDI. These results need to be confirmed both with a larger dataset and after adjustment for clinical covariates.
Disclosure: Vincent Young is a consultant for Bio-K+ International, Pantheryx, and Vedanta Biosciences.
OBJECTIVES/GOALS: We investigated the association between gut microbiota features in newly admitted nursing facility (NF) patients and the acquisition of vancomycin-resistant Enterococcus (VRE) and/or resistant Gram-negative bacteria (rGNB) within 14 days. METHODS/STUDY POPULATION: Patients were recruited at 6 Michigan NFs from 09/16-08/18. VRE or rGNB colonization status was determined by culture swabs collected from multiple body sites at enrolment, day 7, and day 14. Our analysis focused on patients with no colonization at baseline, a perirectal swab collected at baseline, and at least one follow-up visit. The V4 hypervariable region of the 16S rRNA gene from bacterial DNA in each sample was PCR-amplified and sequenced on the MiSeq platform. Sequencing results were then processed with the mothur bioinformatics pipeline to classify bacterial taxa present in each sample. Taxa typically associated with the skin microbiota were removed. The primary outcome was acquisition of VRE and/or rGNB within 14 days. Exposures of interest included patient and microbiota characteristics. RESULTS/ANTICIPATED RESULTS: Among 61 patients, 18 (30%) acquired AROs within 14 days of enrolment (3 VRE, 13 rGNB, 2 both) (Table 1). The baseline microbiota features differed significantly in those who acquired a new ARO. Of the major 8 phyla found across samples, patients who acquired an ARO were depleted in the number of phyla present (5.74 ± 1.20 vs 5.06 ± 1.43; p = 0.037) (Fig. 1). The log10-transformed relative abundance of Enterococcus was enriched in patients who acquired an ARO (−0.32 ± 1.47) compared to those who did not (−1.68 ± 1.76; p = 0.021) (Fig. 2). Patients who did not acquire an ARO tended to harbour more butyrate-producing bacterial taxa and strict anaerobes, although the differences were not statistically significant (relative abundance of butyrate producer: 29.49 ± 22.09 vs 22.05 ± 17.76; anaerobes: 64.78 ± 23.54 vs 53.68 ± 27.61). DISCUSSION/SIGNIFICANCE OF IMPACT: Microbiota metrics calculated from perirectal samples are predictive of ARO acquisition. The clinical utility of perirectal samples thus warrants further assessment.
To evaluate whether incorporating mandatory prior authorization for Clostridioides difficile testing into antimicrobial stewardship pharmacist workflow could reduce testing in patients with alternative etiologies for diarrhea.
Single center, quasi-experimental before-and-after study.
Tertiary-care, academic medical center in Ann Arbor, Michigan.
Adult and pediatric patients admitted between September 11, 2019 and December 10, 2019 were included if they had an order placed for 1 of the following: (1) C. difficile enzyme immunoassay (EIA) in patients hospitalized >72 hours and received laxatives, oral contrast, or initiated tube feeds within the prior 48 hours, (2) repeat molecular multiplex gastrointestinal pathogen panel (GIPAN) testing, or (3) GIPAN testing in patients hospitalized >72 hours.
A best-practice alert prompting prior authorization by the antimicrobial stewardship program (ASP) for EIA or GIPAN testing was implemented. Approval required the provider to page the ASP pharmacist and discuss rationale for testing. The provider could not proceed with the order if ASP approval was not obtained.
An average of 2.5 requests per day were received over the 3-month intervention period. The weekly rate of EIA and GIPAN orders per 1,000 patient days decreased significantly from 6.05 ± 0.94 to 4.87 ± 0.78 (IRR, 0.72; 95% CI, 0.56–0.93; P = .010) and from 1.72 ± 0.37 to 0.89 ± 0.29 (IRR, 0.53; 95% CI, 0.37–0.77; P = .001), respectively.
We identified an efficient, effective C. difficile and GIPAN diagnostic stewardship approval model.
Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.
OBJECTIVES/SPECIFIC AIMS: Objectives and goals of this study are to (i) determine whether IBS-D patients randomized to either rifaximin or low FODMAP diet show improvement in IBS-related symptoms; and (2) identify using longitudinal analyses how SIBO status and fecal microbiota features associate with response to either rifaximin or low FODMAP dietary intervention. METHODS/STUDY POPULATION: 42 patients ≥ 18 years of age who meet Rome IV criteria for IBS-D will be randomized to receive either rifaximin or low FODMAP diet intervention. The primary outcome will be the proportion of responders to intervention which is defined as ≥ 30% reduction in mean daily abdominal pain or bloating by visual analog scale compared with baseline. Exclusion criteria will include: (a) history of microscopic colitis, inflammatory bowel disease, celiac disease, or other organic disease that could explain symptoms, (b) prior gastrointestinal surgery, other than appendectomy or cholecystectomy > 6 months prior to study initiation, (c) prior use of rifaximin or formal dietary interventions for IBS-D, (d) use of antibiotics within the past 3 months, or (e) use of probiotics within 1 month of study entry. Glucose hydrogen breath tests will be performed at the beginning and end of the trial to evaluate for SIBO. Fecal samples will be collected at 0, 2, and 6 weeks to determine changes in fecal microbial composition and structure. RESULTS/ANTICIPATED RESULTS: This study seeks to examine whether longitudinal analyses of small intestinal and colonic microbiota can subtype IBS-D subjects into clinically relevant phenotypes. A total of 18 subjects have been enrolled into the study. Clinical variables, hydrogen breath test results, and fecal microbiota data are being collected for ongoing analysis. DISCUSSION/SIGNIFICANCE OF IMPACT: Results from this study may help move treatment of IBS from a purely symptom based approach to a more individualized approach by stratifying IBS-D patients into distinct clinical phenotypes which are amenable to targeted therapeutic approaches.
A new occurrence of awaruite, from India, is reported in the ultramafic units of the Dras area which form part of the Indus Suture ophiolites in Kashmir Himalaya. Its formation in these rocks suggests a low-temperature process of serpentinization. Ni and Fe released from olivine and/or pyroxene must have formed awaruite peragenetically next to magnetite and pentlandite.
The rare nickel telluride mineral melonite is identified from Jaduguda uranium ore, Singhbhum Shear Zone, Bihar, India. Its physical, optical, chemical, and paragenetic characteristics are described. The composition as obtained by EPMA is NiTe1.66Bi0.06 or approximately Ni3(Te,Bi)5. The association of melonite with molybenite in this ore deposit is unusual, since melonite is generally associated with tellurides of gold and silver. This is the first reported occurrence of melonite from India.
An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH).
We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital.
Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities.
A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies.
Greenhouse studies were conducted to evaluate potential interactions among glyphosate mixtures with five acetolactate synthase (ALS)-inhibiting herbicides (chlorimuron, imazamox, imazaquin, MON 12,000, or pyrithiobac) for the control of purple nutsedge and sicklepod at two growth stages. Herbicides were tested alone at 0.5X and 1X rates (1X being suggested use rate for these herbicides) and in combination with glyphosate at 560 (0.5X) and 1,120 (1X) g ai/ha on 3-wk-old plants and at 1,120 g/ha on 6-wk-old plants. Glyphosate alone at 1,120 g/ha gave complete control of purple nutsedge and at least 78% control of sicklepod regardless of growth stage. In 3-wk-old purple nutsedge plants, three of the 20 herbicide combinations were antagonistic and 17 combinations were additive, whereas all five combinations were additive in 6-wk-old plants. In sicklepod, eight combinations were antagonistic and 12 combinations were additive in 3-wk-old plants, and all five combinations were antagonistic in 6-wk-old plants. In 3-wk-old plants, the glyphosate (0.5X) plus imazaquin (0.5X) combination resulted in highest antagonism in purple nutsedge control (79%), and the combination of glyphosate (0.5X) plus imazamox (0.5X) resulted in highest antagonism in sicklepod control (54%). These results indicate that mixing chlorimuron, imazamox, imazaquin, MON 12,000, or pyrithiobac with glyphosate does not increase glyphosate efficacy on purple nutsedge or sicklepod.
The change in dielectric constant relaxation time over temperature (35–590 °C) and frequency (45 Hz–5 MHz) in ceramics of Pb0.77K0.115Gd0.115Nb2O6 (PKGN, Tc = 340 °c) has been studied. Powder X-ray diffraction revealed the single-phase formation with orthorhombic crystal structure. The P-E hysteresis loop parameters are Ps = 21.77 μC/cm2, Pr = 17.09 μC/cm2, Ec = 11.86 kV/cm; the piezoelectric constants, Kp = 31.7%, Kt = 47%, d33 = 115 × 10−12 C/N, d31 = −41 × 10−12 C/N, are determined in the material and some transducer applications are discussed. Cole-Cole (Zll vs. Zl) plots showed a non-Debye type relaxation. Conductivity obeyed Jonscher’s universal power law, σ = σ0 + Aωn. The theoretical values of εl and σ are computed using the parameters ‘A(T)’ and ‘n(T)’ (0 < n < 1) and are well fitted with the experimental data. The hopping ion frequency (ωp) and charge carrier concentration (Kl) have been analyzed using Almond-West formalism. The dielectric relaxation processes are associated with localized oxygen vacancies conduction at high frequency region. A long-range conductivity by Gd3+ ions is found to be predominant at low frequency region. The activation energies from impedance and modulus formalisms revealed the ionic type conduction in PKGN.
Cobalt ferrite nanoparticles were prepared by co-precipitation method and were heat treated at 100 oC, 200 oC, 400 oC and 600 oC for 2 h to increase the particle size. Phase purity of samples was confirmed by X-ray diffraction. Scherrer formula calculations showed crystallite size varied from 12 to 24 nm when heated from 100 oC to 600 oC. Transmission electron microscopy reveals a uniform and narrow particle size distribution about 12 nm for as-prepared cobalt ferrite particles. Room temperature saturation magnetization was found to vary from 40.8 to 67.0 emu/g as the particle size increased from12 nm to 24 nm. Increase in saturation magnetization with increase in particle size was attributed to the presence of magnetic inert layer on the surface of nanoparticles. Inert layer thickness calculated at 10 K and 300 K was 6 Å and 11 Å respectively. The dielectric properties ε’, tanδ, Z and θ have been studied as a function of frequency and particles size. For the 12 nm grain size, the dielectric constant is one order higher than that of bulk cobalt ferrite. Increase in the grain size showed an increase in the dielectric constant. The increase in the conductivity with grain size is mainly due to the grain size effects. The present study shows that the dielectric properties can be tailor-made to suit the requirement of a particular application by controlling the grain size.