George L. Donati

George L. Donati

Contact Info

GeorgeDonati-128x150

Associate Research Professor

B.S. 2000, University of the Guaxupé Educational Foundation (Brazil)
M.Sc. 2006, Federal University of São Carlos (Brazil, Prof. Joaquim A. Nóbrega)
Ph.D. 2010, Wake Forest University (Prof. Bradley T. Jones)
Postdoctoral Research Associate 2010-2012, Federal University of São Carlos (Brazil, Prof. Joaquim A. Nóbrega)
Teacher-Scholar Postdoctoral Fellow 2012-2013, Wake Forest University

 

Office: Salem Hall, Room 011B
Phone: (336) 758-4815
Email: donatigl@wfu.edu

Home Page: https://sites.google.com/site/georgedonati7

Jones / Donati Research Group

Dr. Jones and Dr. Donati Research Group image

Dr. Jones and Dr. Donati Research Group

Research Description

Analytical Chemistry Instrumentation for Trace Element Analysis

Trace elements play a critical role in many processes and the demand for the determination of their chemical forms and concentrations in samples of economic, technological and environmental importance have steadily increased in the last few decades. The significance of such analytes can be observed by the different pieces of legislation establishing their maximum levels in several samples, and their role in the development of new drugs, materials, and processes.

In the Donati lab we develop analytical methods to determine trace levels of metals and nonmetals in such varied samples as biodiesel fuel, superficial waters, plant material, and animal tissue. Our research is focused on three main areas: (i) developing new strategies to improve the performance of spectrochemical instrumentation such as ICP OES, ICP-MS and MIP OES; (ii) designing, constructing and evaluating the analytical performance of low-cost, portable instrumentation for trace element analysis; and (iii) combining modern analytical instrumentation and advanced statistical tools such as principal component analysis and machine learning to study a broad range of issues, from diabetes diagnostics to cancer drug development.

Research Projects

Applying ICP-MS and Machine Learning for Diabetes Diagnostics

Donati Research Figure 1The determination of chemical imbalances typically present at the onset of some aggressive diseases may be used for early diagnosis, which could reduce mortality and contribute for more efficient treatments. An interesting approach on nutritional and toxicological studies is the determination of minerals in fingernails and toenails. The concentrations of certain elements in these samples can be significantly different in healthy or sick individuals, and some works have demonstrated the efficiency of using nails as biological markers. Elemental concentration changes are often very subtle, especially at the onset of diseases. Therefore, a sensitive method is required to detect such small changes and contribute to diseases diagnostics. In this project, we take advantage of ICP-MS’ high sensitivity and multi-element capabilities, combined with machine learning models based on advanced techniques such as random forest, support vector machine, k-nearest neighbors, naïve Bayes, logistic regression, penalized logistic regression, and linear discriminant analysis, to develop a non-invasive method to diagnose diabetes.

Reference

Carter, J. A.; Long, C. S.; Smith, B. P.; Smith, T. L. and Donati, G. L. Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes. Expert Syst. Appl., 115, 2019, 245-255.

New Calibration Methods to Improve Accuracy in Trace Element Analysis

Donati Research Figure 2 Analytical spectrochemical methods such as ICP OES, ICP-MS and MIP OES have increasingly become fundamental tools in modern quantitative analysis, with applications in such a broad range of fields as environmental, materials science, medicine and ecology. Despite all their success, accuracy issues, usually associated to the analysis of complex matrices, have prevented these methods from realizing their full potential. In this project, we develop new calibration strategies that are capable of minimizing matrix and/or spectral interfering effects to significantly improve accuracy in ICP OES, ICP-MS and MIP OES determinations. Calibration methods such as the interference standard (IFS), standard dilution analysis (SDA), multi-energy calibration (MEC), multi-isotope calibration (MICal), and multispecies calibration (MSC) are part of our effort to improve the performance of modern analytical instrumentation. These are simple strategies, which require no expensive instrument modifications nor laborious sample preparation procedures, and can significantly improve accuracy and sample throughput in trace element analysis.

References

Carter, J. A.; Barros, A. I.; Nóbrega, J. A. and Donati, G. L. Traditional calibration methods in atomic spectrometry and new calibration strategies for inductively coupled plasma mass spectrometry. Front. Chem., 6, 2018, Art. 504, 25p.

Jones, W. B.; Donati, G. L., Calloway Jr., C. P. and Jones, B. T. Standard dilution analysis. Anal. Chem., 87(4), 2015, 2321-2327.

Virgilio, A.; Gonçalves, D. A.; McSweeney, T.; Gomes Neto, J. A.; Nóbrega, J. A. and Donati, G. L. Multi-energy calibration applied to atomic spectrometry. Anal. Chim. Acta, 982, 2017, 31-36.

Williams, C. B. and Donati, G. L. Multispecies calibration: a novel application for inductively coupled plasma tandem mass spectrometry. J. Anal. At. Spectrom., 33 (5), 2018, 762-767.

Naturally Occurring Plasma Species Combined with Supervised and Unsupervised Learning to Improve Accuracy in Spectrochemical Analysis

Donati Research Figure 3In this project we employ plasma native species such as Ar, H, N, N2+, O and OH to identify changes in the atomization/ionization/excitation environment and use that information to correct for matrix effects. Unsupervised machine learning based on principal component analysis (PCA) and affinity propagation clustering (AP) is used to find subtle patterns in the background species data, which is then used to identify and quantify matrix effects, contributing to informed decisions on calibration strategies that will ensure accurate results. Supervised machine learning based on random forest, support vector machine with a radial basis function kernel, k-nearest neighbors, among others is then used to correct for signal bias caused by matrix effects. This approach has the potential to improve accuracy on the fly. Background species are monitored during the analysis and then used to automatically correct for discrepancies between the calibration standards and the sample, which may significantly improve the performance of important methods such as ICP OES, MIP OES and ICP-MS.

References

Lowery, K. L.; McSweeney, T.; Adhikari, S. P.; Lachgar, A. and Donati, G. L. Signal correction using molecular species to improve biodiesel analysis by microwave-induced plasma optical emission spectrometry. Microchem. J., 129, 2016, 58-62.

Williams, C. B.; Jones, B. T. and Donati, G. L. Naturally occurring molecular species used for plasma diagnostics and signal correction in microwave-induced plasma optical emission spectrometry. J. Anal. At. Spectrom., 33 (7), 2018, 1224-1232.

Publications

Carter, J. A.; Barros, A. I.; Nóbrega, J. A. and Donati, G. L., Traditional calibration methods in atomic spectrometry and new calibration strategies for inductively coupled plasma mass spectrometry, Front. Chem. 6 (2018) Art. 504, 25p.

Babos, D. V.; Virgilio, A.; Costa, V. C.; Donati, G. L. and Pereira-Filho, E. R., Multi-energy calibration (MEC) applied to laser-induced breakdown spectroscopy (LIBS), J. Anal. At. Spectrom. 33 (10) (2018) 1753-1762.

Carter, J. A.; Long, C. S.; Smith, B. P.; Smith, T. L. and Donati, G. L., Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes, Expert Syst. Appl. 115 (2019) 245-255.

Machado, R. C.; Silva, A. B. S.; Donati, G. L. and Nogueira, A. R. A., Multi-energy calibration as a strategy for elemental analysis of fertilizers by microwave-induced plasma optical emission spectrometry, J. Anal. At. Spectrom. 33 (7) (2018) 1168-1172.

Williams, C. B.; Jones, B. T. and Donati, G. L., Naturally occurring molecular species used for plasma diagnostics and signal correction in microwave-induced plasma optical emission spectrometry, J. Anal. At. Spectrom. 33 (7) (2018) 1224-1232

Williams, C. B. and Donati, G. L., Multispecies calibration: a novel application for inductively coupled plasma tandem mass spectrometry, J. Anal. At. Spectrom. 33 (5) (2018) 762-767.

Virgilio, A.; Nóbrega, J. A. and Donati, G. L., Multi-isotope calibration for inductively coupled plasma mass spectrometry, Anal. Bioanal. Chem. 410(3) (2018) 1157-1162.

Subashchandrabose, S.; Pereira-Filho, E. R. and Donati, G. L., Trace element analysis of urine by ICP-MS/MS to identify urinary tract infection, J. Anal. At. Spectrom. 32 (8) (2017) 1590-1594.

Virgilio, A.; Gonçalves, D. A.; McSweeney, T.; Gomes Neto, J. A.; Nóbrega, J. A. and Donati, G. L., Multi-energy calibration applied to atomic spectrometry, Anal. Chim. Acta 982 (2017) 31-36.

Donati, G. L.; Amais, R. S. and Williams, C. B., Recent advances in inductively coupled plasma optical emission spectrometry, J. Anal. At. Spectrom. 32 (7) (2017) 1283-1296.

Fortunato, F. M.; Vieira, A. L.; Gomes Neto, J. A.; Donati, G. L. and Jones, B. T.,  Expanding the potentialities of standard dilution analysis: Determination of ethanol in gasoline by Raman spectroscopy, Microchem. J. 133 (2017) 76-80.

Quigley, K. M.; Althoff, A. G. and Donati, G. L., Inductively coupled plasma optical emission spectrometry as a reference method for silicon estimation by near infrared spectroscopy and potential application to global-scale studies of plant chemistry, Microchem. J. 129 (2016) 231-235.

Lowery, K. L.; McSweeney, T.; Adhikari, S. P.; Lachgar, A. and Donati, G. L., Signal correction using molecular species to improve biodiesel analysis by microwave-induced plasma optical emission spectrometry, Microchem. J. 129 (2016) 58-62.

Goncalves, D. A.; McSweeney, T. and Donati, G. L., Characteristics of a resonant iris microwave-induced nitrogen plasma, J. Anal. At. Spectrom. 31 (5) (2016) 1097-1104.

Jones, W. B.; Donati, G. L., Calloway Jr., C. P. and Jones, B. T., Standard dilution analysis, Anal. Chem. 87(4) (2015) 2321-2327.

Amais, R. S.; Nóbrega, J. A. and Donati, G. L., The interference standard method: evidence of principle, potentialities and limitations, J. Anal. At. Spectrom. 29 (7) (2014) 1258-1264.

Rocha, D. L.; Batista, A. D.; Rocha, F. R. P.; Donati, G. L. and Nóbrega, J. A., Greening sample preparation in inorganic analysis, Trend Anal. Chem. 45 (2013) 79-92.

Donati, G. L.; Amais, R. S.; Schiavo, D. and Nóbrega, J. A., Determination of Cr, Pb, Ni and V in gasoline and ethanol fuel by microwave plasma optical emission spectrometry, J. Anal. At. Spectrom. 28(5) (2013) 755-759.

Gu, J.; Oliveira, S. R.; Donati, G. L.; Gomes Neto, J. A. and Jones, B. T., Rugged, portable tungsten coil atomic emission spectrometer, Anal. Chem. 83(7) (2011) 2526-2531.