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An interface in physical science or materials science is usually defined as the boundary between two different materials or different physical states of the same matter. Interfaces can be classified into several typical catalogs, such as solid–gas interface, solid–liquid interface, liquid–liquid interface, and solid–solid interface, and so on. Surface is a special interface, formed between material and vacuum. The interface is important and interesting because its properties and behavior can be quite different from the adjacent bulk phases. The emergence of the interesting phenomena is resulted from the thermodynamic constraints enforced by the two-dimensional surface/interface. The different concentrations (or density) and structural arrangements of atoms or molecules in the surface/interface region compared with bulk materials, result in unique physical and chemical properties of interfaces. Interfaces, therefore, are often found to play a central role both in nature and within a variety of different technological applications, devices, and industrial processes1. For example, solid–gas interfaces have been involved in catalytic reactions such as the reduction of harmful gas emissions in catalytic converter in automobiles, producing industrial chemicals through heterogeneous catalytic reactions, thin-film growth during microelectronics processing, etc. Liquid–gas interfaces are important for environmental problems. Liquid–liquid interfaces play a large role in the biological process and many daily life applications including detergents, foods, and paints by the stabilization of emulsions and micro-emulsions. Solid–solid interfaces are especially important in advanced functional materials and devices1, 2.
The nanoscale interfacial properties between functional materials can significantly affect a wide range of device characteristics, especially for modern microelectronics. Such effect would either hinder the performance of electronics or actually open opportunities for innovative design of new type of devices. For example, transition metal oxides, which exhibit rich material properties due to the unique characteristics of their outer d electrons, are promising for the next-generation oxide electronics2-5. Both atom reconstruction and electron reconstruction, as well as spin, orbital, and charge coupling at the oxide interfaces have led to novel interface physics as well as emergent phenomena6-10. The conductivity, as well as superconductivity observed at the interface between the two wide-bandgap insulators of LaAlO3 and SrTiO3, are remarkable examples8, 11-15. Heterojunctions that hybrid with semiconductors have also demonstrated significant roles in photocatalysts16-18. It is thus of great importance to characterize the electronic states at the interface.
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Because of the importance of interfaces, many different tools or methods have been developed to characterize the chemical composition, geometrical arrangements, and various properties, including mechanical properties and processes (such as thickness, roughness, clusters/particles dimensions, and distribution, friction, fracture, strength, strain, stress, deformation properties, fatigue resistance, wear, etc.), physical properties and processes (e.g., density, crystallization, physical inter-diffusion, dielectric and magnetic properties, energy density, etc.), chemical properties and chemical processes (e.g., elemental and molecular compositions of the layers, size, and orientation of individual molecules, adhesion, corrosion, passivation, interfacial interactions, chemical diffusion, barrier properties, etc.), as well as optical properties and processes (including refractive indices, spectral reflectivity, and transmittance, optical absorption properties, etc.)19. The characterization methods can be classified into different groups regarding the properties of objects studied, or detection features of tools. The classifications of the surface and interface analyzing techniques are available in several reviews and books19-23. Following the classification presented in refs. 19, 20, we listed the main surface and interface analysis techniques in Table 1, according to the detection features. Typical analytical methods for buried interfaces include electron detection methods, photon detection methods, neutron detection methods, ion detection methods, and scanning probe methods.
Photon detection methods Electron detection methods Neutron detection methods Ion detection methods TXRF and standing wave XRF,
Energy dispersive and wavelength-dispersive XRF,
Glancing-incidence (GI)-X-ray reflectivity (XRR),
GI-X-ray diffuse scattering,
GI-resonant X-ray scattering, GI-XRD GI-XAFS, OES, Laser ablation or sputter depth profiling,
Ion-beam spectrochemical analysis,
RAIRS,
ATR,
SEIRA,
ATR-FTIR,
Surface Raman spectroscopy,
Optical reflectivity and ellipsometry,
SFG,
SXAPS,
IPESXPS,
AES,
EELS,
APS,
SEM,
EF-TEM,
STM,
LEEDNeutron reflectivity
Neutron diffraction and scatteringSIMS,
Electron impact (EI)-SNMS,
Laser-SNMS,
RBS,
LEIS,
ERDA,
NRA,
FIMPlease refer to Appendix 1 for the explanation of acronyms -
Emerging electron microscopy techniques, based on scanning transmission electron microscopy (STEM) and/or electron energy loss spectroscopy (EELS) (such as 4D-STEM, cryo-STEM, and monochromated EELS) are very useful tools for probing functional interfaces in energy materials (as shown typically in Fig. 1)24, 25. Spatially resolved EELS is capable of examining the conduction band structure and has been used to study the electronic changes at perovskite oxide heterointerfaces7, 26, 27. However, EELS is usually equipped with the expensive facility of STEM and is also limited by the time-consuming, destructive sample preparation necessary for generating electron transparent specimens. Cathodoluminescence (CL) is capable of probing the emission properties at the interface area28, 29. Photoemission spectroscopy (PES), including X-ray photoemission spectroscopy (XPS) and Ultraviolet photoemission spectroscopy (UPS) are powerful to investigate the valence band structure while X-ray absorption spectra (XAS) is frequently used for conduction band investigation. However, they are usually classified as "surface sensitive techniques", due to the limitation of electron mean free path. This review aims to introduce the development and extension of these techniques to probe the buried electronic states at the interface.
Fig. 1 Overview of probing functional interfaces in energy materials using emerging electron microscopy techniques.
Reproduced with permission24. Copyright 2019, Wiley
Important roles of interfaces
Characterization of interfaces
Typical analytical methods for buried interfaces
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The quantitative probing method is to compare experimental PES spectra to model spectra as one material is grown on another. The experimental setup thus requires an integrated ultra-high vacuum (UHV) system that is equipped with the integration of thin-film growth techniques and spectra characterization probes, as shown in Fig. 3. A variety of thin film deposition techniques have been developed to meet the requirement of the high demand of different thin-film materials, including molecular beam epitaxy (MBE)61-66, pulsed laser deposition67-70, metal-organic chemical vapor deposition71-73, atomic layer deposition74, 75, etc. Oxide MBE has been particularly developed to fabricate novel oxide heterostructure and superlattice76. A typical oxide MBE chamber is usually equipped with different evaporation metal sources from either effusion cell or e-beam evaporator as well radio frequency plasma source to generate active oxygen atoms. Quartz crystal microbalance (QCM) would be adopted to determine the flux rate of the metal evaporation sources. In situ and real-time reflection high energy electron diffraction (RHEED)77-79 is chosen to precisely monitor the atomic layer growth as the pattern intensity oscillates simultaneously with the surface morphology during the growth. By comparing the RHEED patterns appearing at various zone axes, the symmetry of the surface structure can be determined; and by comparing those from the substrate and from the film, the registry relationship can be speculated. A differential pumping for the RHEED gun (electron source) is needed to prevent cathode filament degradation in the high partial oxygen pressure during oxide growth. During growth, the chamber is usually cooled by running water or liquid nitrogen.
Fig. 3 Integrated ultra-high vacuum system for the quantitative modeling of photoemission spectra for interface states.
Left: Thin film growth techniques (e.g., MBE). Right: In-situ photoemission spectroscopy (XPS and/or UPS)By integrating the thin film growth system with spectroscopy characterization techniques connected under UHV channels, the fleshly grown thin films then have the privilege to undergo the characterization of electronic properties without exposing to the air, thus keeping the natural features produced during growth. For the in situ characterization of XAS spectra that requires the tuning of photon energy, it would be necessary to attach a thin film growth chamber on the beam station of synchrotron radiation light source facility80, 81. In regular laboratories, one can combine the thin film growth chamber with the analysis chamber that contains PES probing techniques82. For the PES experiment using the XPS instrument, X-rays with photon energy more than 1 kV are generated by bombarding either magnesium or aluminum anodes with high-energy electrons. For the PES experiment using UPS equipment, ultraviolet photons are produced using a gas discharge lamp. Helium gas is usually used to emit photons with energies of 21.2 eV (He Ⅰ) and 40.8 (He Ⅱ). Recently, oxide MBE growth systems have been combined with angle-resolved photoemission to prompt the new research stage of strongly correlated materials83-90.
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Even though PES spectra are considered surface sensitive, the mean-free path, λ, of the photoelectrons is large enough that the spectra will sample several monolayers into the sample. For thin films, the measured spectra will then consist of a superposition of emission from the substrate, from any interfacial states that may be present, and from the film, with each weighted by electron escape depths. The detailed analysis procedures are listed as follows.
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Before examining any possible interface states, we first assume that there are no interface states present. We then compare the measured spectra to the model spectra consisting of a superposition of spectra from the substrate and that from the film. Assuming layer-by-layer growth, and taking into account the electron escape probability $e^{ - \frac{d}{\lambda }}$91, the spectral intensity I as a function of thin-film thickness d (as shown in Fig. 4a) can be calculated as92-94
$$ I_{without{\kern 1pt} interface}^{model}\left( d \right) = I_0^{Substrate}e^{ - \frac{d}{\lambda }} + I_0^{Film}\left( {1 - e^{ - \frac{d}{\lambda }}} \right) $$ (1) Fig. 4 The sketch models used for the quantitative simulation of spectra.
a A film with a thickness of d grown on the substrate, assuming no interface states. b. A film with a thickness of d grown on the substrate, considering interface states. dif and dis are the thickness if the film and the substrate, respectively, involved to form the interface layer$I_0^{Substrate}$ and $I_0^{Film}$ represent the experimental intensity of the "bulk spectra" of the substrate and the film, respectively. In the previous reports92-94, the spectral intensity of the thickest film (usually not thinner than 20 monolayers) was used as the "bulk" spectrum. Here, we further take into account the thickness D of the thickest film and $I_D^{Film}/\left({1 - e^{ - \frac{D}{\lambda }}} \right)$ 56 is adopted to represent the intensity of the "bulk" spectrum instead. Thus Eq. (1) is modified as
$$I_{without{\kern 1pt} interface}^{model}\left( d \right) = I_0^{Substrate}e^{ - \frac{d}{\lambda }} + I_D^{Film}\left( {1 - e^{ - \frac{d}{\lambda }}} \right)/\left( {1 - e^{ - \frac{D}{\lambda }}} \right) $$ (2) The IMFP λ in Eq. (2) can be estimated using the plots in Fig. 2b or the formulas therein. It can also be calculated based on the reports by Tanuma et al.95, 96. The thickness d of the thinner film can be determined on the attenuation of the core-level photoemission line from the substrate97:
$$ d = - {\it{ln}}\frac{{I_{after}}}{{I_{before}}} $$ (3) where $I_{before}$ and $I_{after}$ are the spectral intensities of the XPS core-level photoemission line from the substrate before and after the thin film deposition with a thickness d. The thickness D of the thickest film can be probed using a variety of techniques, including microscope.
Difference spectra are then taken between the experimental $I^{expt}$ and model spectra $I_{without{\kern 1pt} interface}^{model}$:
$$ \varDelta I\left( d \right) = I^{expt}(d) - I_{without{\kern 1pt} interface}^{model}(d) $$ (4) If there are no obvious features in the difference spectra $\varDelta I\left(d \right)$, an electronically sharp interface without additional electronic state could be claimed.
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Any difference features between the measured and model spectra may then result from the interfacial electronic structure. If an interface state exists, Eq. (2) can be changed to be: [modified from refs. 92, 93
$$\begin{array}{ll}I_{with{\kern 1pt} interface}^{model}(d) = I_0^{Substrate}e^{\frac{{ - \left( {d + d_{is}} \right)}}{\lambda }} + I_D^{Film}\frac{{1 - e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}}}{{1 - e^{\frac{{ - D}}{\lambda }}}}\\ \qquad\qquad\qquad\qquad+ \,I_0^{Interface}\left[ {1 - e^{\frac{{ - \left( {d_{is} + d_{if}} \right)}}{\lambda }}} \right]e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}\end{array} $$ (5) where dis and dif are the thickness of the substrate and the deposited film, respectively, involved to form the interface layer (as shown in Fig. 4b); I0Interface is the spectral intensity for the interface layer, assuming a semi-infinite slab having the interface electronic structure; and d is the deposited thickness of the film. With the assumption that the experimental spectra contain interface states, we can use $I^{expt}$ for $I_{with{\kern 1pt} interface}^{model}$ in Eq. (5). Therefore, Eq. (5) becomes
$$ \begin{array}{ll}I^{expt}(d) = I_0^{Substrate}e^{\frac{{ - \left( {d + d_{is}} \right)}}{\lambda }} + I_D^{Film}\frac{{1 - e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}}}{{1 - e^{\frac{{ - D}}{\lambda }}}}\\ \qquad\qquad\quad+ \,I_0^{Interface}\left[ {1 - e^{\frac{{ - \left( {d_{is} + d_{if}} \right)}}{\lambda }}} \right]e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}\end{array} $$ (6) Thus, the intensity of the interface state spectrum can be determined as
$$ I_0^{Interface} = \frac{{I^{expt}(d) - \left\{ {I_0^{Substrate}e^{\frac{{ - \left( {d + d_{is}} \right)}}{\lambda }} + I_D^{Film}\frac{{\left[ {1 - e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}} \right]}}{{1 - e^{\frac{{ - D}}{\lambda }}}}} \right\}}}{{\left[ {1 - e^{\frac{{ - \left( {d_{is} + d_{if}} \right)}}{\lambda }}} \right]e^{\frac{{ - \left( {d - d_{if}} \right)}}{\lambda }}}} $$ (7) -
Once the parameters λ, and film thickness $(d, D)$ are determined, the only variable parameters in Eq. (7) are dis and dif, which are the components of the interface layer thickness contributed by the substrate and the film, respectively. For certain interface model structure dis and dif (Fig. 4), one can calculate a set of $I_0^{Interface}$ data ($I_{0[d_1]}^{Interface}$, $I_{0[d_2]}^{Interface}$, $I_{0[d_3]}^{Interface}$, …) using the available experimental data $I^{expt}(d)$ at different d $(d_1, d_2, d_3 \ldots)$, based on Eq. (7). Different valuables of dis and dif for interface layer thickness can be used to obtain different $I_0^{Interface}$ sets of data. The most likely interface layer structure dis and dif would correspond to the particular $I_0^{Interface}$ set of data, in which case, $I_{0[d_1]}^{Interface}$, $I_{0[d_2]}^{Interface}$, $I_{0[d_3]}^{Interface}$… are similar to each other.
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Once the best-fit interface layer model is determined, the interface state spectrum can be finally calculated by averaging the $I_0^{Interface}$ set of data with the corresponding values of the dis and dif parameters for the best-fit model.
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The above-mentioned quantitative modeling has been used to study the interfaces between Fe3O4 and other transition-metal oxides, specifically NiO and CoO. All of these oxides are of significant interest in spintronics. In particular, Fe3O4–NiO and Fe3O4–CoO have been proposed as an ingredient in all-oxide tunneling spin valves98. Fe3O4 is a metallic ferrimagnet, and both NiO and CoO are insulating antiferromagnets. The exchange biasing effect99-103 in which the hysteresis loop of a ferro- or ferrimagnet is shifted asymmetrically along the field axis when in contact with an antiferromagnetic material, has been observed for both interfaces, making them interesting for spintronics. NiO and CoO have the same rocksalt crystal structure, and, although Fe3O4 has the inverse spinel structure, both structures share a common face-centered-cubic oxygen sublattice, where the lattice mismatch is only 0.55% between Fe3O4 and NiO and 1.45% between Fe3O4 and CoO. Despite the fact that NiO and CoO have very similar bulk electronic properties, it is interesting that, the Fe3O4 (001) − NiO (001) interface exhibits a sharp interface without obvious interface electronic state88, while the Fe3O4 (001)–CoO (001) interface displays non-trivial electronic state and the interface state spectrum was determined using the above mentioned quantitative modeling by comparing two interface layer models82. In one case, the interface layer consisted of one monolayer of the substrate Fe3O4 plus one monolayer of the film CoO; in the other case, the interface layer consisted of only one monolayer of the film CoO. The determination of the better-fit interface model was based on the observation of the degree of similarity among the generated three spectra of $I_{0[d_1]}^{Interface}$, $I_{0[d_2]}^{Interface}$, $I_{0[d_3]}^{Interface}$, using each model. It was concluded that the first case where the interface layer consists of one monolayer of the substrate Fe3O4 plus one monolayer of the film CoO is closer to the actual case. The interface states spectrum determined using the best-fit model is shown in Fig. 5.
Fig. 5 The determined spectrum of interface states between Fe3O4-CoO based on the quantitative modeling method, compared with the substrate spectrum of Fe3O4 and the film spectrum of CoO.
Reproduced with permission (adapted from82). Copyright 2018, American Physical Society
Requirement of experimental setup
Quantitative modeling of experimental spectra
Step 1: (Assuming no interface states)
Step 2: (Considering interface states)
Step 3: (Calculating interface states using different film thickness)
Step 4: (Determining interface state spectrum based on the best-fit interface layer model)
Case studies of the quantitative modeling of spectra for interface states
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1. Electron Excitation AES, Auger electron spectroscopy BIS, Bremsstrahlung isochromat spectroscopy (or ILS, ionization loss spectroscopy)EDXS, Energy-dispersive X-ray spectroscopy EELS, Electron energy loss spectroscopy EFTEM, Energy-filtered transmission electron microscopy ESD, Electron-stimulated desorption (or EID, electron-induced desorption) ESDIAD, Electron-stimulated desorption ion angular distribution IPES, Inverse photoemission spectroscopy LEED, Low-energy electron diffraction RHEED, Reflection high-energy electron diffraction SXAPS, Soft X-ray appearance potential spectroscopy (or APS, appearance potential spectroscopy) SAM, Scanning Auger microscopy SEM, Scanning Electron microscopyEF-TEM, Energy-filtering transmission electron microscopy 2. Ion Excitation ERDA, Elastic recoil detection analysis GDMS, Glow discharge (GD) mass spectrometry GD-OES, Glow discharge (GD) optical emission spectroscopy (OES) IAES, Ion (excited) Auger electron spectroscopy IBSCA, Ion beam spectrochemical analysis (or SCANIIR, surface composition by analysis of neutral and ion impact radiation or BLE, bombardment induced light emission) INS, Ion neutralization spectroscopy LEIS, Low-energy ion scattering (or ISS, Ion-scattering spectroscopy) NRA, Nuclear reaction analysis RBS, Rutherford back-scattering spectroscopy (or HEIS, high-energy ion scattering) SIMS, Secondary-ion mass spectrometry (SSIMS, static secondary-ion mass spectrometry) (DSIMS, dynamic secondary-ion mass spectrometry) SNMS, Secondary neutral mass spectrometry 3. Photon Excitation ELL, Ellipsometry LA, Laser ablation LIBS, Laser-induced breakdown spectroscopy (or LIPS, Laser-induced plasma spectroscopy) RAIRS, Reflection-absorption infrared spectroscopy (or IRRAS, infrared reflection-absorption spectroscopy, or IRAS, infrared absorption spectroscopy, or ERIRS, external reflection infrared spectroscopy) ATR, Attenuated total reflection FTIR, Fourier transform infred spectroscopy SERS, Surface-enhanced Raman scattering SFG, Sum frequency generation SHG, (optical) Second-harmonic generation SNOM, Scanning near-field optical microscopyTXRF, Total reflection X-ray fluorescence (XRF) analysis UPS, Ultraviolet photoelectron spectroscopy XPS, X-ray photoelectron spectroscopy (or ESCA, electron spectroscopy for chemical analysis) XRD, X-ray diffraction XAFS, X-ray absorption fine structure 4. Neutral Excitation FABMS, Fast-atom bombardment mass spectrometry 5. Thermal Excitation TDS, Thermal desorption spectroscopy 6. High-Field Excitation AP, Atom probe FIM, Field ion microscopy IETS, Inelastic electron tunneling spectroscopy STM, Scanning tunneling microscopy STS, Scanning tunneling spectroscopy 7. Mechanical Force AFM, Atomic force microscopy