Measuring Chemical Shifts with Energy-Dispersive X-ray Spectroscopy

TL;DR

The paper demonstrates that energy-dispersive X-ray spectroscopy (EDS) can detect chemical shifts with high precision by leveraging large solid-angle solid-state detectors and optimized calibration and processing. It develops a rigorous error framework that separates absolute energy calibration from peak-to-peak precision and shows that precision is limited primarily by peak-centering fits, not the calibration itself. Through detector dead-time optimization, processing-time tuning, and careful sample preparation, the authors map chemical shifts in Al, Ti, and W and demonstrate spatially resolved chemical-state information via EDS, complemented by EELS data. This work expands the operational parameter space for chemical-shift analysis with EDS, offering a complementary approach to EELS for chemical-state mapping across varying accelerating voltages, thicknesses, and atomic numbers.

Abstract

Electron microscopy prevalently uses energy-dispersive x-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS) for elemental analysis. EDS and EELS energy resolutions are commonly between 30-100 eV or 0.01-1 eV, respectively. Large solid angle EDS detector technology has increased collection efficiency to enable precision spectroscopy via averaging of 0.02-0.1 eV. This improved precision gives access to chemical shifts; examples are shown in compounds of Al, Ti, and W. EDS can now detect chemical information in a complementary parameter space (accelerating voltage, thickness, atomic number) to that covered by EELS.

Figures (11)

• Figure 1: The progress of x-ray detector's solid angle size over time, and a fit function for past interpolation and future extrapolate fitzgerald_solid-state_1968nicholls_comparison_1988zaluzec_progress_1991lyman_high-performance_1994fiorini_new_1997kotula_results_2008zaluzec_innovative_2009schlossmacher_nanoscale_2010noauthor_product_2011ohnishi_ultrahighly_2016howe_collection_2017zaluzec_quantitative_2022. Silicon drift detectors (SDDs) have smaller collecting capacitors and larger detector areas. This gives access to faster acquisition speeds and larger solid angles than SiLi detectors.
• Figure 2: Fit a) and residual b) from the data shown in main text Figure 1c. Black and gray represent W metal and oxide respectively. Normalized counts are used for the EDS data to allow comparison between metal and oxide. Unnormalized counts are used to calculate the residual to counts value shown in b).
• Figure 3: Paralyzable detector output count rate as a function of the input count rate for the indicated dead time.
• Figure 4: The NiO$_x$ strobe peak as a function of the detector chains processing time. The processing time indicated here is a parameter that is proportional to the processing time used in Oxford's detector chain. Larger (longer) processing times produce a smaller noise peak monotonically. The longest processing times were used in nearly all acquisitions to ensure the best energy resolution.
• Figure 5: The FWHM of the noise peak, C, O, Ni$_L$, and Ni$_K$ as a function of the measured energy of each peak. The FWHM and the center are measured by curve fitting to main text Equation 1. The fit parameters are the Fano factor and strobe peak FWHM, the Fano parameter compares well to known values for silicon mckay_electron_1953shockley_problems_1961mazziotta_electronhole_2008ramanathan_ionization_2020.