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Tauc plot

The Tauc plot is a graphical technique employed to estimate the optical bandgap energy of amorphous, disordered, or polycrystalline semiconductors by analyzing their ultraviolet-visible (UV-Vis) absorption spectra. It is constructed by plotting ( \alpha h \nu )^n versus photon energy h \nu, where \alpha is the absorption coefficient, h is Planck's constant, \nu is the light frequency, and the exponent n is typically 2 for indirect allowed transitions or 1/2 for direct allowed transitions; the bandgap E_g is determined by extrapolating the linear region of the plot to the point where ( \alpha h \nu )^n = 0.^[1]^[2] The method was originally developed by J. Tauc, R. Grigorovici, and A. Vancu in 1966 while studying the optical properties of evaporated amorphous germanium films, where they plotted \omega^2 \epsilon_2 (with \omega as angular frequency and \epsilon_2 as the imaginary part of the dielectric function) against photon energy to reveal a bandgap of approximately 0.88 eV at 300 K, indicating transitions without strict momentum conservation akin to those in crystalline counterparts.^[1] This approach stemmed from theoretical assumptions of parabolic density-of-states near band edges and constant matrix elements for optical transitions, adapting concepts from crystalline semiconductors to amorphous materials where long-range order is absent.^[3] Over time, the Tauc plot evolved into a versatile tool, often adapted using the Kubelka-Munk function F(R_\infty) for diffuse reflectance measurements on powdered samples, expressed as [F(R_\infty) h \nu]^n versus h \nu, enabling bandgap assessment in photocatalysts like modified TiO₂.^[2] In practice, the Tauc plot is widely applied in materials science to characterize semiconductors for applications in photovoltaics, optoelectronics, and photocatalysis, providing a simple yet effective way to quantify bandgaps ranging from the UV (e.g., 3.2 eV for anatase TiO₂) to the near-IR (e.g., 1.1 eV for amorphous silicon).^[2] It distinguishes between direct and indirect transitions by selecting the appropriate n value that yields the most linear plot in the absorption onset region, and it has been extended to nanostructures, thin films, and metal-organic frameworks despite ongoing refinements.^[3] The technique's popularity arises from its reliance on standard UV-Vis spectroscopy, making it accessible for evaluating electronic structure in disordered systems where traditional methods like photoluminescence may falter.^[2] Despite its ubiquity, the Tauc method has limitations, particularly when sub-bandgap absorption (Urbach tails) from defects or doping is significant, leading to overestimation of E_g if baseline corrections are neglected.^[2] It assumes idealized density-of-states and transition probabilities that may not hold for all materials, such as highly crystalline or multiphase samples, prompting alternatives like the Cody plot [\alpha / h \nu]^{1/2} versus h \nu for certain amorphous cases where Tauc's bandgap exceeds the true value.^[3] Recent studies emphasize consistent extrapolation procedures and validation against theoretical models to enhance accuracy, especially for emerging nanomaterials.

Overview

Definition and Purpose

A Tauc plot is a graphical representation derived from optical absorption spectra, used to estimate the Tauc bandgap (E_g), which is the optical bandgap of disordered, amorphous, or indirect semiconductors where conventional crystalline analysis techniques are ineffective due to structural irregularities.^[1]^[3] It transforms absorption coefficient data (α) as a function of photon energy (hν) or wavelength into a semi-logarithmic or linear form that highlights the absorption edge, enabling the bandgap to be identified as the x-intercept of the extrapolated linear region near the transition onset.^[2]^[3] The primary purpose of the Tauc plot is to facilitate the extrapolation of the absorption edge, revealing the energy threshold for optical transitions and providing key insights into the electronic band structure of materials affected by disorder, such as tail states in valence and conduction bands.^[3] This method is essential for characterizing semiconductors in fields like photovoltaics and optoelectronics, where understanding the bandgap governs light absorption efficiency and charge carrier dynamics, distinguishing it from direct measurement approaches that assume momentum conservation in crystalline systems.^[2]^[1] Named after physicist J. Tauc, the approach was formally defined in his 1966 study on amorphous germanium, and its enduring popularity stems from the simplicity of implementation using standard UV-Vis spectroscopy, requiring only absorption data without complex sample preparation.^[1]^[2]

Historical Development

The Tauc plot method was introduced in 1966 by J. Tauc, R. Grigorovici, and A. Vancu in their paper titled "Optical Properties and Electronic Structure of Amorphous Germanium," published in Physica Status Solidi B. This work presented the method as a graphical technique to extrapolate the optical bandgap from absorption data in amorphous materials, specifically applied to evaporated films of amorphous germanium (a-Ge). Developed in the context of early studies on non-crystalline solids, the method addressed the shortcomings of crystalline semiconductor models, which assumed strict momentum conservation in optical transitions. By plotting the data in a way that linearizes the absorption edge, it enabled clearer identification of the bandgap in disordered systems like a-Ge, marking a pivotal shift in the analysis of amorphous semiconductors and facilitating broader research into their electronic structure. The technique gained significant traction in the 1970s and 1980s alongside the rapid advancement of amorphous silicon (a-Si) for photovoltaic applications, particularly after the 1975 demonstration of substitutional doping in glow-discharge-deposited hydrogenated amorphous silicon (a-Si:H) by Spear, Le Comber, and colleagues,^[4] and the subsequent fabrication of the first a-Si solar cell by Carlson in 1976. During this period, the Tauc plot became a standard tool for characterizing bandgap variations in a-Si:H films, aiding optimization for solar cell efficiency and stability. By the 1990s, the method had been extended to evaluate indirect bandgaps and further disordered materials beyond classic amorphous semiconductors, reflecting its versatility in handling momentum-relaxed transitions. In the 2000s, it was integrated into routine ultraviolet-visible (UV-Vis) spectroscopy protocols for analyzing thin films and powdered samples, solidifying its role as a benchmark in materials science for bandgap determination. The original 1966 paper remains highly influential, with over 12,000 citations as recorded in scholarly databases.

Theoretical Foundation

Tauc's Relation

The Tauc relation provides the fundamental theoretical basis for analyzing the optical absorption near the bandgap in amorphous semiconductors. It expresses the absorption coefficient \alpha as a function of the photon energy h\nu, given by the equation

\alpha h\nu = B (h\nu - E_g)^n,

where B is a constant, E_g is the optical bandgap energy, and the exponent n characterizes the nature of the electronic transitions (with specific values discussed elsewhere). This relation was originally derived for amorphous germanium (a-Ge), employing n=2. The derivation stems from a density-of-states model tailored to amorphous materials, assuming parabolic energy bands near the valence and conduction band edges. In crystalline semiconductors, momentum conservation restricts transitions, but in amorphous structures, disorder leads to a relaxation of this rule, allowing the momentum matrix element to be treated as constant and independent of photon energy. Under these conditions, the absorption coefficient is proportional to the joint density of states (JDOS) for transitions across the bandgap. For parabolic band edges, the individual densities of states scale as \sqrt{E}, and without k-conservation, the JDOS near E_g follows a (h\nu - E_g)^2 dependence, yielding the power-law behavior captured by the Tauc relation with n=2. The constant B incorporates the squared matrix element and prefactors from the JDOS, remaining approximately constant near the absorption edge. This model assumes band-to-band transitions without excitonic effects.^[5] The relation holds primarily for photon energies h\nu close to E_g, where the power-law approximation dominates the absorption edge. To facilitate bandgap determination, the equation is rearranged to linearize the data: plotting (\alpha h\nu)^{1/n} versus h\nu produces a straight line in the relevant region, with extrapolation to the x-axis (where y=0) yielding E_g. This transformation highlights the linear regime and enables graphical extraction of the bandgap.^[5] Subsequent generalizations extended the framework to other amorphous materials and transition types by varying n, broadening its applicability beyond the initial a-Ge case.

Direct and Indirect Transitions

In the context of the Tauc plot, direct optical transitions occur when an electron is excited from the valence band to the conduction band with negligible change in crystal momentum, corresponding to a vertical transition in k-space at the Brillouin zone center (k ≈ 0).^[6] These transitions do not require phonon involvement for momentum conservation, making them efficient in direct-bandgap materials such as gallium arsenide (GaAs).^[6] For allowed direct transitions, the exponent n in the Tauc relation is 1/2, leading to a plot of (\alpha h\nu)^2 versus h\nu that yields a linear region near the absorption edge, from which the bandgap is extrapolated. Forbidden direct transitions, which involve additional selection rule violations, use n = 3/2, resulting in a steeper dependence and a plot of (\alpha h\nu)^{2/3} versus h\nu. Indirect optical transitions, in contrast, involve a change in crystal momentum (k ≠ 0), requiring phonon assistance to conserve total momentum through absorption or emission of lattice vibrations.^[6] This process is characteristic of indirect-bandgap materials like silicon (Si), where the conduction band minimum and valence band maximum are at different k-points.^[6] For allowed indirect transitions, n = 2, and the Tauc plot uses (\alpha h\nu)^{1/2} versus h\nu to obtain the linear extrapolation for the bandgap. Forbidden indirect transitions employ n = 3, plotting (\alpha h\nu)^{1/3} versus h\nu, though these are less commonly observed due to their weaker probability. The choice of n is primarily determined by the material's band structure: direct-gap semiconductors like GaAs typically use n = 1/2, while indirect-gap ones like Si use n = 2. For unknown or complex materials, n is selected through trial-and-error, plotting with different values (e.g., 1/2, 2) and choosing the one that provides the best linear fit in the high-absorption region. In amorphous materials, the distinction between direct and indirect transitions is blurred by localized band tail states arising from disorder, yet the Tauc method provides an effective approximation by assuming parabolic-like bands.^[7] The Urbach tail, an exponential extension of absorption below the bandgap due to these tails, influences the low-energy side of the spectrum but does not affect the bandgap extrapolation from the linear portion of the Tauc plot.^[7] Notably, for disordered semiconductors, an empirical choice of n = 2 is often applied, even when the crystalline counterpart exhibits direct transitions, reflecting the indirect-like character imposed by structural disorder.

Methodology

Data Collection

The primary experimental technique for obtaining the optical absorption data required for a Tauc plot is ultraviolet-visible (UV-Vis) spectroscopy, which measures either transmittance (T) or reflectance (R) spectra of the sample. For transparent or semi-transparent samples, transmittance is recorded by passing light through the material and calculating the absorption coefficient α from the relation α = (1/d) ln(1/T), where d is the sample thickness; this derivation stems from the Beer-Lambert law assuming negligible reflection.^[8] In cases where both transmittance and reflectance are measured, particularly for thin films, an approximation accounting for multiple reflections is often applied: α ≈ (1/d) ln[(1 - R)^2 / T].^[9] Sample types dictate the measurement approach. For thin films, transmission spectroscopy is common using substrates like glass, while spectroscopic ellipsometry provides the complex refractive index from which α = 4πk / λ (with k as the extinction coefficient and λ the wavelength) is derived, offering high precision without direct thickness dependency in some models.^[10] Powders and opaque solids typically employ diffuse reflectance spectroscopy, where the Kubelka-Munk function F(R) = (1 - R)^2 / (2R) serves as a proxy for α, assuming constant scattering coefficients.^[11] Accurate thickness measurement, often via profilometry or ellipsometry, is critical for absolute α values in transmission-based methods, as errors directly propagate to bandgap estimates.^[8] Instrumentation generally involves double-beam spectrophotometers equipped with deuterium and tungsten lamps, covering wavelengths from 200 to 800 nm to capture the absorption edge.^[12] Accessories such as integrating spheres (e.g., for diffuse reflectance) or Praying Mantis attachments ensure collection of scattered light from irregular surfaces.^[11] Corrections are essential: substrate absorption is subtracted by measuring bare substrate spectra, while scattering in powders is mitigated using standards like BaSO₄ or Spectralon for baseline normalization.^[12] A key procedural step involves converting measured wavelengths λ (in nm) to photon energy hν (in eV) using hν = 1240 / λ, facilitating direct plotting against absorption data.^[8] High spectral resolution (e.g., 1 nm step size) is prioritized near the absorption onset to resolve the band edge accurately, and baseline subtraction—shifting spectra to zero absorbance at low energies—enhances signal-to-noise ratio.^[11]

Plot Construction and Bandgap Extraction

The construction of a Tauc plot begins with processing the raw optical absorption data to obtain the product of the absorption coefficient and photon energy, αhν. The absorption coefficient α is typically derived from transmittance measurements using α = (1/d) ln(I_0/I), where d is the sample thickness and I_0/I is the incident-to-transmitted intensity ratio.^[8] For reflectance data in powders, the Kubelka-Munk function F(R) = (1 - R)^2 / (2R) may be used as a proxy for α.^[11] The photon energy hν is calculated from wavelength λ via hν = 1240 / λ (in eV when λ is in nm). This αhν term captures the energy-dependent absorption behavior near the band edge. Next, the exponent n in the Tauc relation is selected based on the nature of the electronic transitions in the material: n = 1/2 for direct allowed transitions, n = 2 for indirect allowed transitions, n = 3/2 for direct forbidden, or n = 3 for indirect forbidden, with direct and indirect allowed being the most common choices for semiconductors.^[2]^[13] The transformed quantity (αhν)^{1/n} is then computed for data points in the absorption edge region. This transformation linearizes the absorption onset according to the Tauc model, facilitating bandgap determination. The Tauc plot is generated by plotting (αhν)^{1/n} on the y-axis against hν on the x-axis, focusing on the near-edge region where absorption rises sharply, typically corresponding to 10-20% of the total absorption increase. Software such as Origin, MATLAB, or Excel is commonly employed for data import, transformation, and visualization; for instance, in Origin, users can create a new column for (αhν)^{1/n} using user-defined functions and generate the scatter plot. The plot often reveals a linear portion in the rising edge, indicative of the fundamental transitions.^[14]^[2] To extract the optical bandgap E_g, the linear region is identified visually or via preliminary fits, ensuring it captures the steepest absorption onset while excluding sub-bandgap noise. A linear regression is performed on this region, with the equation y = m(hν - E_g), where y = (αhν)^{1/n} and m is the slope. The bandgap is obtained by extrapolating the best-fit line to the x-intercept, where y = 0, yielding E_g in eV. Fit quality is assessed using the coefficient of determination R^2, with values > 0.99 indicating a robust linear region; uncertainties in E_g are reported from the standard error of the fit, often ±0.01-0.05 eV depending on data precision. For an indirect bandgap material like silicon, the plot of (αhν)^{1/2} versus hν exhibits a straight line in the relevant range, and E_g is the x-intercept, typically around 1.1 eV for amorphous silicon.^[14]^[15]^[16] The process is sensitive to the selection of the linear fitting range, as improper inclusion of the Urbach tail—the exponential sub-bandgap absorption due to disorder—can distort the fit. A common error is incorporating the Urbach tail into the linear region, which inflates E_g by 0.1-0.5 eV due to the altered slope and intercept; to avoid over-linearization, the range should be confined to the sharp edge, with iterative checks for maximal R^2 and minimal residuals. Reporting E_g with associated uncertainties from multiple fits enhances reliability.^[7]^[14]

Applications

In Amorphous Semiconductors

The Tauc plot serves as a primary tool for determining the optical bandgap energy E_g in amorphous semiconductors, where the lack of long-range order renders crystal momentum conservation ill-defined, leading to indirect-like optical transitions. Unlike crystalline counterparts, these materials exhibit extended band tails due to disorder, which the Tauc method effectively captures by extrapolating the linear portion of (\alpha h\nu)^{1/2} versus h\nu to the energy axis. This approach is particularly valuable for non-crystalline solids, enabling precise bandgap extraction from UV-Vis absorption data without relying on phonon-assisted processes typical in crystals.^[2] Pioneered in the study of evaporated amorphous germanium (a-Ge), the Tauc plot yielded an E_g \approx 0.88 eV, demonstrating its utility for elemental amorphous semiconductors. In hydrogenated amorphous silicon (a-Si:H), widely used in thin-film photovoltaics, the method routinely determines E_g \approx 1.7 eV, a value critical for optimizing light absorption in solar cells where a-Si:H layers capture higher-energy photons efficiently.^[17]^[18] Similarly, for chalcogenide glasses like As_2S_3, Tauc plots reveal bandgaps around 2-3 eV, aiding in applications such as optical memory devices by accounting for the pronounced Urbach tails induced by structural disorder.^[19] A key advantage of the Tauc plot in amorphous semiconductors lies in its ability to incorporate disorder-induced exponential band tails, often using (\alpha h\nu)^{1/2} to model the indirect nature of transitions dominated by random momentum scattering rather than strict k-conservation. This makes it superior for materials with high defect densities, where direct plotting methods fail. In practical applications, it monitors hydrogenation effects in a-Si, where incorporating hydrogen passivates dangling bonds and shifts E_g from approximately 1.5 eV in low-hydrogen content films to 1.7 eV in optimized device-quality a-Si:H, enhancing stability and performance.^[20]^[21] The method is also integrated into quality control for amorphous silicon thin-film transistors (TFTs), where consistent E_g extraction ensures uniform channel material properties for display technologies.^[22] The Tauc plot remains widely used in peer-reviewed studies on amorphous semiconductors employing UV-Vis spectroscopy, underscoring its enduring reliability.

In Nanostructured Materials

In nanostructured materials, the Tauc plot plays a crucial role in characterizing quantum confinement effects, which lead to an increase in the optical bandgap energy (E_g) as particle size decreases below the exciton Bohr radius. For instance, in CdSe quantum dots, the bandgap shifts from approximately 1.7 eV in the bulk material to 2.5 eV for particles with a 2 nm diameter, as determined from the extrapolation of Tauc plots derived from absorption spectra.^[23] This size-dependent blue shift is a direct manifestation of quantum confinement, allowing researchers to tune optical properties for applications in optoelectronics. For powdered nanomaterials, where transmission spectroscopy is impractical, the Tauc plot is adapted using diffuse reflectance spectroscopy to estimate the absorption coefficient and extract E_g reliably.^[24] Representative examples illustrate the versatility of Tauc plots in nanostructured systems. In ZnO nanoparticles, the bandgap varies from 3.2 eV to 3.8 eV depending on synthesis conditions and size, enabling enhanced UV absorption for photocatalytic or sensing applications.^[25] Similarly, TiO_2 nanotubes exhibit a bandgap of around 3.0–3.2 eV, as revealed by Tauc analysis of reflectance data, which supports their use in photocatalysis by improving charge separation under UV illumination.^[26] In perovskite nanocrystals, such as CsPbBr_3, Tauc plots confirm bandgaps near 2.3 eV, facilitating their integration into light-emitting diodes (LEDs) for efficient color-tunable emission.^[27] The procedure for constructing Tauc plots in nanomaterials often requires adaptations to account for structural imperfections. Due to surface defects and trap states, exponents other than the standard 2 for direct allowed or 1/2 for indirect allowed transitions may be used—such as 3/2 for direct forbidden transitions—to achieve a linear fit in the plot, providing a more accurate E_g extrapolation.^[28] Additionally, Tauc-derived bandgaps are frequently correlated with particle sizes measured via transmission electron microscopy (TEM), enabling validation of confinement models across ensembles of nanoparticles.^[29] A key insight from Tauc plots in these systems is the observed blue shift in the absorption edge intercept, which aligns with predictions from quantum confinement models like the Brus equation, confirming the theoretical framework without needing full derivations. This validation is particularly evident in size-series experiments where E_g scales inversely with radius. In two-dimensional materials, such as MoS_2, the method reveals a transition from an indirect bulk bandgap of 1.2 eV to a direct monolayer value of 1.8 eV, supporting applications in flexible electronics and optoelectronics.^[30]^[31]

Limitations and Alternatives

Key Assumptions and Criticisms

The Tauc plot method relies on several key assumptions that underpin its derivation from the theory of optical transitions in semiconductors. One fundamental assumption is that the momentum matrix element remains constant and independent of photon energy, allowing the absorption coefficient to be modeled without energy-dependent variations in transition probabilities. Another is the presence of sharp band edges, which neglects the exponential tails of absorption (Urbach tails) that occur due to disorder or defects. The method also presumes a parabolic density of states near the band edges, leading to the characteristic square-root dependence in the Tauc relation for indirect transitions. Finally, the approach is valid only in the vicinity of the optical bandgap energy E_g, where interband transitions dominate and the linear extrapolation applies. These assumptions, however, have drawn significant criticism for their limited applicability, particularly outside amorphous semiconductors. In polycrystalline materials, the Tauc plot often underestimates the bandgap due to the exponential decay of absorption from Urbach tails and scattering effects; for instance, in polycrystalline TiO_2, errors of up to approximately 0.5 eV have been reported compared to more accurate methods like photoluminescence spectroscopy. The method is insensitive to mixed direct and indirect transitions, as seen in commercial TiO_2 powders like P25, where overlapping contributions from direct (~3.3 eV) and indirect (~2.9 eV) processes distort the plot's linearity. Additionally, baseline absorption from defects or impurities skews results in modified semiconductors, such as those with doping or surface alterations, leading to unreliable extrapolations. Common practical issues further exacerbate inaccuracies in bandgap extraction. Selecting an inappropriate exponent n in the Tauc relation—such as using n = 1/2 for materials with indirect transitions—can yield erroneous results, while poor identification of the linear range in the plot amplifies extrapolation errors. The method is also unsuitable for determining excitonic gaps, as in ZnO where excitonic features at ~3.43 eV interfere with the interband onset. A 2018 study highlighted that misuse in doped systems results in significant errors due to sub-bandgap absorption.^[2] A 2023 perspective further questions the Tauc plot's routine use beyond amorphous materials, noting its failure in systems with non-parabolic bands or strong excitonic effects, and as of 2025, ongoing literature debates, including automated algorithms for non-translucent semiconductors, recommend refinements or alternatives for precise bandgap assessment in crystalline oxides.^[32]^[33]

Alternative Approaches

Several alternative methods have been developed to address limitations in the Tauc plot approach, particularly for materials where assumptions about constant matrix elements or sharp absorption edges do not hold, such as in amorphous or indirect semiconductors. These techniques often provide more accurate bandgap extraction by isolating specific absorption features or incorporating additional optical data.^[2] The Cody plot offers an improved analysis for amorphous semiconductors like hydrogenated amorphous silicon (a-Si:H), where the Urbach tail dominates low-energy absorption. By plotting the absorption coefficient \alpha on a logarithmic scale against photon energy h\nu (i.e., \log_{10} \alpha vs. h\nu), this method separates the exponential Urbach tail from the higher-energy absorption regime, allowing extrapolation to define the optical gap as the energy where \alpha = 10^3 cm^{-1}. This approach yields more reproducible bandgap values compared to the Tauc method, especially for thin films where thickness effects influence the apparent gap.^[34]^[35] Derivative spectroscopy enhances resolution for bandgap determination by computing the first derivative of the absorption coefficient with respect to photon energy, d\alpha / d(h\nu). Peaks in this derivative curve correspond to the onset of strong absorption, providing a direct indicator of the bandgap E_g without relying on linear extrapolations that can be sensitive to noise or tail states. This technique is particularly effective for direct semiconductors with sharp absorption edges, such as GaN, where it isolates the transition energy more precisely than traditional plots.^[36]^[37] Other experimental and computational methods complement optical absorption-based techniques. Spectroscopic ellipsometry derives the bandgap from the complex refractive index, modeling the dielectric function to extract E_g without direct transmission measurements, which is advantageous for opaque or thin films like metal oxides. Photoluminescence (PL) spectroscopy identifies the bandgap via the onset of emission spectra, where the low-energy tail of the PL peak reflects the minimum energy for radiative recombination in direct-gap materials such as quantum dots. For theoretical validation, GW-BSE (Green's function with screened Coulomb interaction and Bethe-Salpeter equation) computations predict quasiparticle bandgaps and optical spectra, offering ab initio benchmarks that align closely with experimental values in materials like ZnFe_2O_4.^[38]^[39]^[40] Hybrid approaches refine the Tauc method itself to mitigate specific artifacts. A baseline-corrected Tauc plot, proposed in 2023, subtracts low-energy absorption contributions (e.g., from defects or scattering) from the UV-Vis spectrum prior to plotting, idealizing the data for more accurate linear fits and bandgap extraction in semiconductors like cubic boron arsenide. The Derivative of Ineffective Thickness Method (DITM) extends this by analyzing the derivative of transmission intensity to probe secondary optical transitions beyond the primary bandgap, useful for distinguishing multiple absorption processes in thin films.^[41]^[42] These alternatives often reduce bandgap estimation errors by 0.1-0.3 eV compared to standard Tauc plots, particularly in oxides where tail states or indirect transitions broaden the absorption edge. For powdered samples, combining the Kubelka-Munk function—which transforms diffuse reflectance into an effective absorption spectrum—with derivative analysis sharpens the bandgap onset, as demonstrated in TiO_2 photocatalysts. By 2025, machine learning models have emerged as complements to these methods, fitting full absorption spectra to predict bandgaps with high fidelity using ensemble techniques trained on diverse datasets, enhancing accuracy for novel materials like transparent conductors.^[43]^[12]^[44]

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Spectroscopic ellipsometry (SE) provides a widely applicable method for determining such properties without many of the complications and limitations that ...
[37]
Measurement and analysis of photoluminescence in GaN
Mar 25, 2021 · Photoluminescence (PL) spectroscopy is a powerful tool in studying semiconductor properties and identifying point defects.
[38]
GW‐BSE Calculations of Electronic Band Gap and Optical Spectrum ...
Jan 9, 2020 · The study uses GW-BSE to calculate ZnFe2O4's optical properties, finding that the optical spectrum depends on cation distribution and magnetic ...
[39]
Idealizing Tauc Plot for Accurate Bandgap Determination of ... - arXiv
Jun 13, 2023 · We propose a new method that involves idealizing the absorption spectrum by removing its baseline before constructing the Tauc plot.Missing: corrected | Show results with:corrected
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Exploring secondary optical transitions: a study utilizing the DITM ...
Apr 2, 2024 · To compare the band-gaps obtained from the Tauc model and the DITM method, the band-gap is initially determined using the Tauc model. In Fig. 5, ...
[41]
Accurate Bandgap Energy of Rare-Earth Niobates
Feb 10, 2023 · This analysis reveals that the traditional method of determination based on the Tauc plot (21) underestimates the bandgap energy. Our method for ...
[42]
Machine learning method improves semiconductor band gap ...
Feb 10, 2025 · This new ensemble-learning method predicts the physical properties of unknown materials, using data based on measurements of known compounds.