George Donati
    A tungsten filament excites sodium atoms
    Trace elements can be quantified by atomic emission spectrometry, which is based on exciting atoms and determining their concentration in a sample from the intensity of radiation emitted as they return to the ground state. In this picture, a tungsten filament at approximately 3000 K was used to excite sodium atoms, which emitted their characteristic yellow light at 588.995 and 589.592 nm. A piece of electrical tape was used to block the view of the tungsten filament and prevent the saturation of the commercial camera used to capture this picture. A cloud of emitting atoms can be observed around the electrical tape. The intense light observed at the top of the picture is background radiation emitted by the tungsten filament and reflected off the glass atomizer cell.
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George L. Donati, Ph.D.

Deputy Director - Laboratory of Inorganic and Nuclear Chemistry
Ph.D., Chemistry, Wake Forest University (2010)
Postdoctoral training: Federal University of São Carlos, Brazil (2012)
518-474-7161

Research Interests

Trace elements play a critical role in many processes and the demand for determining their chemical forms and concentrations in samples of economic, technological, and environmental importance has steadily increased in the last few decades. According to the International Union of Pure and Applied Chemistry (IUPAC), trace elements are defined as any element present at an average concentration < 100 parts per million atoms (ppma) or < 100 μg/g. The significance of such analytes can be observed by the different pieces of legislation establishing their maximum allowed levels in drinking water and some food products, and their role in the development of new drugs, materials, and processes.

In the Donati lab, we develop analytical strategies to accurately determine trace elements in superficial waters, consumer products, plant material, and animal tissue. Our research is focused on two main areas: (i) the development of calibration strategies to improve the performance of modern spectrochemical methods such as inductively coupled plasma optical emission spectrometry (ICP-OES), ICP mass spectrometry (ICP-MS), and microwave-induced plasma OES (MIP-OES); and (ii) the application of advanced statistical and machine learning tools, along with trace element data, to study a broad range of issues, from matrix effects in plasma-based analytical methods to anemia of inflammation and diabetes mellitus.

Some recent contributions from our lab include new calibration methods such as multi-energy calibration (MEC), multi-isotope calibration (MICal), multispecies calibration (MSC), and automated standard dilution analysis (SDA). Machine learning tools and naturally occurring species in the plasma atomization source have been used by our group to identify and minimize matrix effects and improve the accuracy of ICP-OES determinations. Advanced statistical tools and machine learning strategies have also been used, in combination with trace element data, to evaluate sheep genetic crossing and nutrition (a collaboration with colleagues from Brazil), iodine concentrations in human milk based on type of diet (a collaboration with colleagues at East Carolina University and the University of North Carolina at Greensboro), and the effects of diabetes mellitus on the elemental composition of toenails (a collaboration with colleagues at Wake Forest University).

Select Publications
Ingham JR, Jones BT, Donati GL. Automated standard dilution analysis using a four-port switching valve for fast inductively coupled plasma optical emission spectrometry determination. J. Anal. At. Spectrom. 2023; 38 (12): 2538-2546. DOI: 10.1039/D3JA00295K
Donati GL. Advanced statistical tools and machine learning applied to elemental analysis associated with medical conditions. In M.A. Zezzi-Arruda and J.R. Jesus (vol. eds.), ICP-MS and Trace Element Analysis as Tools for Better Understanding Medical Conditions (part of D. Barceló, ed., Wilson and Wilson’s Comprehensive Analytical Chemistry). 2022; 97 (Elsevier): Amsterdam. DOI: 10.1016/bs.coac.2022.02.002
Higuera JM, Silva ABS, Henrique W, Esteves SN, Barioni W Jr, Donati GL, Nogueira ARA. Effect of genetic crossing and nutritional management on the mineral composition of carcass, blood, leather, and viscera of sheep. Biol Trace Elem Res. 2021; Nov;199 (11): 4133-4144. DOI: 10.1007/s12011-020-02543-8
Pubmed Web Address
Carter JA, O'Brien LM, Harville T, Jones BT, Donati GL. Machine learning tools to estimate the severity of matrix effects and predict analyte recovery in inductively coupled plasma optical emission spectrometry. Talanta. 2021; Feb 1:223 ((Pt 2)): 121665. DOI: 10.1016/j.talanta.2020.121665
Pubmed Web Address
Carter JA, Long CS, Smith BP, Smith TL, Donati GL. 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. 2019; 115 245-255. DOI: 10.1016/j.eswa.2018.08.002
Virgilio A, Gonçalves DA, McSweeney T, Gomes Neto JA, Nóbrega JA, Donati GL. Multi-energy calibration applied to atomic spectrometry. Anal Chim Acta. 2017; Aug 22 (982): 31-36. DOI: 10.1016/j.aca.2017.06.040
Pubmed Web Address
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