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2. Evolution of 'International Advanced Research Center for Powder Metallurgy and New Materials (ARCI)', a Unique Centre for Translating Materials Research to Technology

Author

Vijay, R., Rao, D. Srinivasa, Johnson, Roy, Gopalan, R., Rao, Tata Narasinga, Mudali, U. Kamachi, Editor-in-Chief, Basu, Bikramjit, Editorial Board Member, Chattoraj, I., Editorial Board Member, Prasad, N. Eswara, Editorial Board Member, Manna, Indranil, Editorial Board Member, Gokhale, Amol A., Editorial Board Member, Reddy, G. Madhusudan, Editorial Board Member, Divakar, R., editor, Murty, S. V. S. Narayana, editor, and Srikanth, S., editor

Published
2024
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15. Microstrain induced deviation from N\'eel's 1/d behaviour: Size-dependent magnetization in Bi1-xCaxFe1-yTiyO3-delta nanoparticles

Author

Mocherla, Pavana S. V., Sahana, M. B., Abdelhamid, Ehab, Hajra, Debarati, Nadgorny, B., Naik, R., Gopalan, R., Rao, M. S. Ramachandra, Nanda, B. R. K., and Sudakar, C.

Subjects
Condensed Matter - Materials Science
Abstract

Magnetization of antiferromagnetic nanoparticles is known to generally scale up inversely to their diameter (d) according to N\'eel's model. Here we report a deviation from this conventional linear 1/d dependence, altered significantly by the microstrain, in Ca and Ti substituted BiFeO3 nanoparticles. Magnetic properties of microstrain-controlled Bi1-xCaxFe1-yTiyO3-delta (y = 0 and x = y) nanoparticles are analyzed as a function of their size ranging from 18 nm to 200 nm. A complex interdependence of doping concentration (x or y), annealing temperature (T), microstrain (epsilon) and particle size (d) is established. X-ray diffraction studies reveal a linear variation of microstrain with inverse particle size, 1/d nm-1 (i.e. epsilon.d = 16.5 nm.%). A rapid increase in the saturation magnetization below a critical size dc ~ 35 nm, exhibiting a (1/d)^alpha (alpha ~ 2.6) dependence, is attributed to the influence of microstrain. We propose an empirical formula M is proportional to (1/d)epsilon^beta (beta ~ 1.6) to highlight the contributions from both the size and microstrain towards the total magnetization in the doped systems. The magnetization observed in nanoparticles is thus, a result of competing magnetic contribution from the terminated spin cycloid on the surface and counteracting microstrain present at a given size. Large magnetodielectric response of ~ 9.5 % is observed in spark plasma sintered pellets with optimal size and doping concentration, revealing a strong correlation between magnetic and ferroelectric order parameters., Comment: 44 pages, 15 figures

Published
2017

47. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories

Author

Carobene, A, Cabitza, F, Bernardini, S, Gopalan, R, Lennerz, J, Weir, C, Cadamuro, J, Carobene, Anna, Cabitza, Federico, Bernardini, Sergio, Gopalan, Raj, Lennerz, Jochen K., Weir, Clare, Cadamuro, Janne, Carobene, A, Cabitza, F, Bernardini, S, Gopalan, R, Lennerz, J, Weir, C, Cadamuro, J, Carobene, Anna, Cabitza, Federico, Bernardini, Sergio, Gopalan, Raj, Lennerz, Jochen K., Weir, Clare, and Cadamuro, Janne

Abstract

Objectives The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. Methods We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. Results The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. Conclusions In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Published
2023

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