ReviewA dozen useful tips on how to minimise the influence of sources of error in quantitative electron paramagnetic resonance (EPR) spectroscopy—A review
Introduction
Electron paramagnetic resonance (EPR) spectroscopy is a very useful analytical technique for the detection of paramagnetic species in chemical, physical and biological systems. One of the important aspects of EPR is to determine the concentration of the radical species, particularly in biological systems. The aim of this review is to provide a detailed overview of the quantitative analysis of samples containing radical species by means of the quantitative EPR spectroscopy.
As pointed out by Hyde [1] over 40 years ago: “of all the measurements one can make with EPR equipment, the determination of spin concentration is the most difficult”. This fact was fully confirmed in the results obtained from the international experiments carried out in 1962 (coordinated by Kohnlein [2]) and 1991–1992 (coordinated by Yordanov and Ivanova [3]), which clearly demonstrated the essential difficulties in quantitative EPR spectroscopy. In principle, experimental errors in quantitative EPR measurements for a given laboratory, and a given EPR spectrometer, may be reduced in carefully performed experiments to between 2 and 5% [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. However, in practice, quantitative analysis of the same sample in different laboratories produced results, which in the worst cases were incompatible and in others gave an uncertainty of between 100 and 200% [2], [3], [4], [12], [13], [16], and others up to 500% [10], [12], [13], [16]. No satisfactory explanation for this discrepancy has been found at present.
A multitude of sources of error influences the accuracy and reproducibility in quantitative EPR spectroscopy (see elsewhere, [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]). The error sources can be divided into two large groups: (i) primary error sources (including sample- and instrumental-associated factors); and (ii) secondary error sources (including data processing-, EPR standards-calibration-, and human- associated factors). The list of instrumental and sample-associated variables which can affect quantitative EPR measurement is very extensive [7], and the majority of these errors occur simultaneously and synergistically. The essential problem is that some of these sources of error may cause significant systematic and/or non-systematic errors in quantitative EPR measurements. The majority of such error sources can be controlled by the EPR spectrometer operators. However, certain sample-associated errors can be influenced partially, or controlled directly by the EPR spectroscopy users who are making quantitative measurement but who are not EPR spectroscopy specialists/operators. These errors involve mainly sample shape, in the case of the bulk solid-state materials, and the sample packing procedure in the case of solid-state powder or polycrystalline materials.
The most effective way to minimise the influence of such error sources in quantitative EPR spectroscopy would be to use the same standardised procedures for all EPR measurements and post-recording spectra manipulations. However, the crucial problem in obtaining improved accuracy and reproducibility lies in maximising the quality of the input data and not in the subsequent computational procedures. For example, the double integration of the EPR spectra can be accomplished with a precision of 2% or better, when a computer-interfaced spectrometer is used [9].
As clearly shown in the literature [4], [7], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], the primary, crucial and the most important error sources error in quantitative EPR measurements from the sample-associated factors can be the influence of the variation of the sample material (dielectric constant), sample size and shape, sample tube, and sample orientation and positioning within the microwave cavity on the EPR signal intensity. Variation in these parameters can cause significant serious errors in the primary phase of quantitative EPR analysis (i.e., data acquisition). Therefore, this topic was recently studied systematically in our EPR laboratory [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].
The main aim of this review is to provide useful suggestions, recommendations and simple procedures to minimise the influence of such the above-selected primary error sources in quantitative EPR measurements. We believe that the tips summarised at the end of this report will be helpful in quantitative EPR practice.
Section snippets
The rocky pathways from sample preparation to the true EPR signal intensity value
It is clear that a multitude of sources of error influence the accuracy and reproducibility of quantitative EPR spectroscopy (see elsewhere, [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]). Previous studies have clearly shown that sample size, shape and positioning within the microwave cavity result in non-uniformity of both
Conclusions
The principal, crucial and the most important error sources in quantitative EPR measurements arising from the sample-associated factors can be the influence of the variation of the (i) sample material (dielectric constant); (ii) sample size and shape; (iii) sample tube wall thickness; and (iv) sample orientation and positioning within the microwave cavity, on the EPR signal intensity. Variation of these parameters can cause significant, serious errors in the primary phase of quantitative EPR
Acknowledgements
This work was supported by Science and Technology Assistance Agency under the contact No. APVT-20-004504, and by the Slovak Grant Agency for Science (VEGA 1/2450/05 and VEGA 1/3579/06). The author is grateful to Dr. M. Valko, Dr. H. Morris, and Professor L. Valko for the fruitful discussions during the course of this work.
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