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Record W4401006516 · doi:10.1093/mam/ozae044.081

Chemical State Analysis of Low-Z Elements by X-ray Photoelectron Spectroscopy (XPS)

2024· article· en· W4401006516 on OpenAlexaff
Mark C. Biesinger

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsX-ray photoelectron spectroscopyChemical stateX-rayMaterials scienceAnalytical Chemistry (journal)State (computer science)ChemistryPhysicsNuclear magnetic resonanceOpticsEnvironmental chemistryComputer science

Abstract

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X-ray photoelectron spectroscopy (XPS) is a surface analysis technique capable of providing elemental and chemical state information from the outer 5 to 10 nanometres of a solid surface. All elements from lithium to uranium can be detected with detection limits ranging from 0.1 to 3 atomic percent (element dependent). When an X-ray of known energy (hν), for example Al(Kα) at 1486.7eV, interacts with an atom, a photoelectron can be emitted via the photoelectric effect. The emitted electron’s kinetic energy (Ek) can be measured and the atomic core level binding energy (Eb) relative to the Fermi level (EF) of the sample is determined by the following equation: where Φsp is the work function of the spectrometer. Chemical information about the sample can be extracted because binding energies are sensitive to the chemical environment of the atom. Chemical environments that deshield the atom of interest (i.e. are bound to strongly electron withdrawing groups) will cause the core electrons of that atom to have increased binding energies. Conversely, decreased binding energies will be measured for core electrons of atoms that withdraw electrons from others. Essentially, binding energy will increase as chemical state number increases. XPS, with its ability to quantify elements and determine chemical states, is used in many branches of materials science, electronics, thin film chemistry, corrosion science, polymer modification, adhesion science, coating chemistry, catalysis, mineral processing chemistry, as well as in exploring fundamental aspects of the chemistry and physics of atoms and molecules. Low-Z elemental analysis by XPS, while in some ways being relatively straightforward, has some limitations with relatively high detection limits, particularly for Li, Be, B and N [1]. Semi-quantitative analysis is possible by measuring the peak areas of specific elemental core lines (I) and by applying appropriate atomic sensitivity factors (S), also known as relative sensitivity factors (RSF), using the general equation: where Cx is the atomic fraction of element x in a sample [1]. The sensitivity factors can be calculated from theory or derived empirically from the analysis of standard samples. The use of standard samples is the preferred method (which is the method applied in the Kratos line of spectrometers). Peak areas are defined by applying an appropriate background correction (e.g. linear, Shirley, Tougaard) across the binding energy range of the peaks of interest. Chemical state analysis for lithium, boron, and fluorine (e.g. Table 1) are generally accomplished using simple curve-fitting techniques of the 1s peak to derive binging energies which can then be compared to literature values in databases such as, for example, the PHI Handbook [2], the NIST XPS database [1], or the X-ray Photoelectron Spectroscopy (XPS) Reference Pages [1]. Beryllium chemical state analysis, in addition to simple curve-fitting of the main Be 1s peak, can also benefit from the use of the modified Auger parameter, which is the sum of the Be 1s peak binding energy and the Be KLL Auger peak kinetic energy. The chemical state analysis of nitrogen, carbon, and oxygen is complicated by the enormous range of chemical states possible in the vast array of organic compounds and additionally for oxygen, the myriad of possible oxide, hydroxide and oxyhydroxide compounds. Analysis of carbon species by XPS is also complicated by the presence of adventitious carbon (AdC), a thin layer of carbonaceous material that deposits on the surface of most air-exposed samples. Recent work has shown that this material is mostly aliphatic in nature with ∼ 25% of carbon species having bonds to oxygen [1]. AdC’s ubiquitous presence on most sample surfaces allows for its use in charge correction procedures but complicates the analysis of organic materials [1]. An average AdC C 1s spectrum (Figure 1), along with calculations based on sample stoichiometry, can be used to subtract out the AdC contribution, allowing for a clearer picture of the chemistries of the organic species present [5]. Typical curve-fitting of AdC starts with a single peak (Gaussian (70 %) – Lorentzian (30 %)), ascribed to alkyl type carbon (C-C, C-H), that is fit to the main peak of the C 1s spectrum. A second peak is added that is constrained to be 1.5 eV above the main peak and is of equal FWHM to the main peak. This higher B.E. peak is ascribed to alcohol (C-OH) and/or ester (C-O-C) functionality. Additional components at higher B.E., C=O, 2.8-3.0 eV above the main peak, and O-C=O, at 3.8-4.3 eV above the main peak are also usually added, again constrained to have the same FWHM as the main peak. The peak ascribed to O-C=O shows the most variation between differing samples. Analysis of materials of a graphitic nature (e.g., graphite, graphene, oxidized graphene, carbon nanotubes etc.) will benefit from advanced curve-fitting procedures. Starting curve-fitting parameters include the main peak asymmetry and π to π* shake-up satellite from a pure graphite standard sample. An example of the application of this is shown in Figure 2 for a graphene oxide material. Further analysis of carbon spectra can be done through using the D-parameter, first reported by Lascovich and colleagues [1], which describes the difference between the minimum and the maximum of the first derivative of the carbon Auger spectrum. This value has been shown to be linearly correlated to the percentage of sp2 versus sp3 hybridized carbon [1]. F 1s binding energies Average of 80 adventitious carbon (AdC) C 1s XPS spectra . An example of curve-fitting of a graphite type system, oxidized graphene .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.273
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2024
Admission routes1
Has abstractno

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