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REVIEW 4 major objections 4 minor 37 references

Electrostatic Interface Engineering with Graphene for Radiation-Tolerant MR-DWELL Dosimeters: A Design Framework

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Tuning the ratio of graphene's quantum capacitance to the h-BN spacer's capacitance cuts radiation-induced resonance shifts threefold and preserves resonant transport to 1 MGy in a quantum-dot tunneling dosimeter.

desk verdict The capacitive-divider screening idea is real and clearly presented, but the headline MGy transport numbers rest on reduced functions that are never shown and on code that contradicts the main text's saturation law. read the letter →

arxiv 2608.06616 v1 pith:BRXH34OY submitted 2026-08-06 math-ph math.MP

Bigul U. Dosymova , Mikhail V. Dolgopolov This is my paper · ORCID
classification math-phmath.MP MSC 82D37
keywords MR-DWELLresonanttunnelingelectrostaticinterfaceengineeringquantumcapacitanceradiationdosimetryhardnesssecond-derivativespectroscopyFLASHdose-rateregime
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a design framework for a radiation-tolerant dosimeter built on a resonant-tunneling heterostructure (an MR-DWELL: a multi-resonant double-barrier quantum-dot-in-a-well stack) topped with a graphene contact separated by an h-BN spacer. The central idea is that graphene's finite density of states gives it a quantum capacitance, and the ratio of that quantum capacitance to the geometric capacitance of the spacer, $\eta = C_Q/C_{\mathrm{geo}}$, sets a screening factor $S = \eta/(1+\eta)$ that controls how much radiation-induced trapped charge perturbs the resonant levels. The model predicts that choosing $\eta \approx 2$ (about a 1 nm h-BN spacer) cuts the effective resonance-shift coefficient threefold, keeps roughly 83% of the peak current with a peak-to-valley ratio near 10 after 1 MGy, and holds the intrinsic RC response near 0.28 ps. The paper further argues that second-derivative spectroscopy, $\mathrm{d}^2I/\mathrm{d}V^2$, offers a practical lock-in readout of the resonance shifts, and that a three-population trapped-charge model opens a path to FLASH dose-rate discrimination even though steady-state dosimetry saturates near 500 Gy. A sympathetic reader would care because the framework turns a known failure mode of resonant-tunneling dosimeters — uncontrolled interface charge growth — into a tunable design variable.

What carries the argument

The load-bearing object is the capacitance ratio $\eta = C_Q/C_{\mathrm{geo}}$ together with the screening factor $S = \eta/(1+\eta)$ it generates. The geometric capacitance is $C_{\mathrm{geo}} = \varepsilon_0\varepsilon_r a_{\mathrm{emit}}/t_d$, with $t_d$ the h-BN spacer thickness, and the graphene quantum capacitance is $C_Q = (2e^2k_BT/\pi(\hbar v_F)^2)\ln\left[2\cosh(\mu/2k_BT)\right]$, fixed in this work at the $E_F \approx 0.3$ eV operating point where the areal value is about 7.2 $\mu$F/cm$^2$. The identity $\alpha_{\mathrm{eff}} = \alpha_i(1-S) = \alpha_i/(1+\eta)$ turns the capacitance ratio into a direct prediction for the dose response, which is then fed through first-order trap-filling kinetics $Q_{\mathrm{trap}}(D) = Q_{\max}[1-\exp(-D/D_0)]$ with $D_0 = 500$ Gy, and through a Breit–Wigner transmission and Landauer current model with two resonances ($E_{10} = 82$ meV, $\Gamma_{10} = 4$ meV; $E_{20} = 126$ meV, $\Gamma_{20} = 7$ meV). A separate three-population trap kinetics (deep, slow, and fast traps) with critical dose rate $\dot{D}_{\mathrm{crit}} = D_{\mathrm{fast}}/\tau_{\mathrm{fast}}$ carries the FLASH dose-rate discrimination hypothesis.

What would settle it

The paper's own validation roadmap doubles as the falsifier: build two otherwise identical MR-DWELL stacks, one with a metal top contact and one with a graphene/h-BN contact, and measure the $\mathrm{d}^2I/\mathrm{d}V^2$ peak shift versus accumulated dose for h-BN thicknesses of 0.5, 1, 2, and 3 nm. The model predicts the low-dose shift coefficient falls as $\alpha_{\mathrm{eff}} = 0.14/(1+\eta)$ meV/mGy and the 1 MGy shift as $70/(1+\eta)$ meV; a clear deviation from these curves, or a dose-dependent motion of the graphene Fermi level comparable to 0.3 eV, would falsify the fixed-$C_Q$ screening picture.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that radiation hardness in a resonant-tunneling dosimeter can be engineered electrostatically rather than metallurgically. Graphene is not a perfect shield: its finite density of states yields a quantum capacitance $C_Q$, so when radiation traps charge at the interface, the induced potential partitions across the series combination of $C_Q$ and the geometric capacitance $C_{\mathrm{geo}}$ of the h-BN spacer. Only the fraction $1-S = 1/(1+\eta)$ of the bare electrostatic perturbation reaches the resonant levels, so the effective low-dose shift coefficient obeys $\alpha_{\mathrm{eff}} = \alpha_i/(1+\eta)$ and the asymptotic high-dose shift is suppressed by the same factor. With $\eta = 2.09$ the unscreened 70 meV asymptotic shift falls to about 23 meV, the peak-to-valley ratio stays near 10, and the device retains about 83% of its peak current at 1 MGy, where the unscreened reference is already strongly degraded. The model further claims a sub-picosecond intrinsic RC time constant near 0.28 ps, a packaging-limited system response near 20 ps, and a design map in which h-BN thickness selects between high-sensitivity, balanced, and maximum-hardness operating regimes.

Load-bearing premise

The load-bearing premise is that radiation-induced trapped charge behaves as a static, uniformly coupled sheet whose electrostatic effect splits cleanly between the graphene quantum capacitance, held fixed at its $E_F \approx 0.3$ eV value, and the geometric capacitance; if irradiation moves the graphene Fermi level or the charge couples unevenly to the resonant states, the identity $\alpha_{\mathrm{eff}} = \alpha_i/(1+\eta)$ fails and the threefold suppression and all 1 MGy metrics collapse.

Editorial extensions

If this is right

  • At $\eta = 2.09$ the model predicts a resonance shift near 23 meV, roughly 83% peak-current retention, and a peak-to-valley ratio near 10 after 1 MGy, moving resonant-tunneling dosimetry from the hundreds-of-gray range into the megagray range.
  • The design rules assign each h-BN thickness to an application class: $\eta < 1.5$ for high sensitivity, $1.5 \lesssim \eta \lesssim 4$ for balanced FLASH-compatible operation, and $\eta > 4$ for maximum hardness at the cost of sensitivity.
  • Because the intrinsic RC time is near 0.28 ps, the speed ceiling shifts to interconnects and front-end electronics, so the estimated 20 ps system response is set by packaging rather than by the tunneling structure itself.
  • The $\mathrm{d}^2I/\mathrm{d}V^2$ readout, which is measurable with standard lock-in techniques, is predicted to track the radiation-induced resonance shifts while remaining well resolved up to 1 kGy in the model.
  • With $D_{\mathrm{fast}} \approx 50$ Gy and $\tau_{\mathrm{fast}} \approx 10$ ms, the fast-trap kinetics give a critical dose rate near $5\times10^3$ Gy/s, spanning $5\times10^2$–$5\times10^4$ Gy/s as the time constant varies from 1 to 100 ms — a window overlapping the FLASH regime — so transient dose-rate discrimination is claimed to be possible even though steady-state dosimetry saturates at 500

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the screening picture survives experimental test, the same $\eta = C_Q/C_{\mathrm{geo}}$ parameter should transfer to any resonant-tunneling sensor whose degradation channel is electrostatic drift of level positions — the framework is about stabilizing levels, not specifically about radiation.
  • The paper fixes $C_Q$ at one Fermi level; a gate-tuned graphene contact would in principle make the screening factor adjustable during operation, letting a single device trade sensitivity for hardness mid-measurement — an extension the authors explicitly reserve for future work.
  • The fast-trap kinetics imply a concrete untested prediction: a pulse at $10^4$ Gy/s should produce a transient $\mathrm{d}^2I/\mathrm{d}V^2$ peak shift that is absent at 0.1 Gy/s even at equal accumulated dose, because the fast-trap population saturates only in the high-rate regime.
  • The radiation-type descriptor $R = \beta/\alpha$ is demonstrated only on synthetic data; the natural next step is to calibrate $\alpha$ and $\beta$ for two well-characterized radiation fields and check whether their ratio is genuinely species-dependent in a real device.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes a graphene/h-BN interface on a multi-resonant double-barrier quantum-dot-in-a-well (MR-DWELL) resonant-tunneling structure, using the ratio η = C_Q/C_geo of graphene quantum capacitance to geometric capacitance as a screening parameter. A capacitive-divider model (Eqs. (1)–(6)) yields the screening factor S = η/(1+η), which reduces the effective radiation-induced resonance shift to α_eff = α/(1+η) (Eq. (13)). The paper claims that for η = 2.09 this suppresses the low-dose shift coefficient threefold, limits the resonance shift at 1 MGy to about 23 meV, retains about 83% of the peak current with a peak-to-valley ratio of about 10, and gives an intrinsic RC response of 0.28 ps. It further introduces d²I/dV² spectroscopy as a readout, a three-population trapped-charge model for FLASH dose-rate discrimination, and design regimes in η. All results are analytical/numerical; no experimental data are presented.

Significance. The electrostatic screening concept is physically plausible and the capacitive-divider algebra is internally consistent. If the quantitative transport metrics were established, the framework would offer a simple design rule (η) for radiation-hard resonant-tunneling dosimeters and a plausible route to FLASH dose-rate discrimination. The paper has strengths worth acknowledging: it explicitly labels its transport functions as 'calibrated reduced models' rather than hiding them, provides Python code for reproducibility, clearly separates steady-state from transient trap populations, and marks the synthetic PCA/LDA classification as purely illustrative. However, the headline quantitative claims — 83% peak-current retention, PVR ≈ 10, and the associated 1 MGy metrics — currently rest on undocumented interpolation functions and on an internal inconsistency in the saturation law used to produce them. The central screening relation (Eq. (13)) is sound, but the transport-level predictions are not yet traceable to the stated Landauer basis.

major comments (4)
  1. [§3.1 and §4.7] The reported peak-current retention and PVR values are not obtainable from the manuscript's stated transport model. Section 3.1 presents Eq. (7) as the physical basis, but then states that 'the reported peak and valley currents are evaluated using calibrated reduced transport functions' without displaying PVR(ΔE) or Inorm(ΔE) or their calibration points. Section 4.7 confirms that these functions are 'not direct Landauer integrals.' Table 3 and Fig. 5 therefore rest on an interpolation whose error at the operating point ΔE ≈ 23 meV is unknown. The authors should either display the reduced functions and calibration points, or evaluate the headline metrics directly from Eqs. (7)–(8), so that the 1 MGy claims become reproducible predictions.
  2. [§4.7 vs. Supplementary Software (electrostatics.py)] There is a direct inconsistency between the claimed physical saturation model and the software used to generate the reported metrics. The main text (Eqs. (18)–(20)) declares the exponential law ΔE(D,η) = ΔE_max(η)[1−exp(−D/D0)] to be the 'genuine physical chain,' with the rational form D/(D+D0) used only for internal calibration. Yet the provided electrostatics.py implements the rational saturation law ΔE(D,η) = ΔE_max(η)·D/(D+D0) for the evaluation of PVR and Inorm. Since the 83% retention and PVR≈10 values are outputs of those routines, the headline transport claims are not demonstrably predictions of the exponential model presented as physical. The authors must either align the software with the declared model or show that the two saturation laws yield the same transport metrics.
  3. [Eqs. (13), (20), and §3.2] The claimed 'threefold suppression' and '23 meV maximum shift' are constructed from assumed inputs rather than derived predictions. Eq. (13) gives α_eff = α/(1+η) using α = 0.14 meV/mGy from the earlier work, and Eq. (20) gives ΔE_max(η) = ΔE_max(0)/(1+η) using ΔE_max(0) = 70 meV. Substituting η = 2.09 returns 23 meV by construction. This is not a problem per se, but the abstract and Section 1 present these as outcomes of the design framework. The manuscript should explicitly distinguish calibrated assumptions (α, ΔE_max(0), D0) from framework predictions, and should quantify how the headline numbers change under the acknowledged uncertainty in these inputs (e.g., α = 0.14 ± 0.01 meV/mGy).
  4. [§3.2 and §11] The fixed-C_Q assumption is load-bearing for the 1 MGy claims and is only acknowledged as a limitation. Equations (12)–(13) take C_Q constant at E_F ≈ 0.3 eV, but Section 3.2 states that the full gate-voltage dependence is reserved for future work, and Section 11 admits that trapped charge would modulate the graphene carrier density and couple C_Q(μ) to trap kinetics. If irradiation shifts the graphene Fermi level, the screening factor S in Eq. (6) changes, and the factor-three suppression no longer follows from Eq. (13). Since the central quantitative conclusions depend on this constancy, the authors should either provide a self-consistent estimate of the C_Q variation under the relevant trapped-charge densities or explicitly restrict the 1 MGy predictions to the fixed-C_Q regime and state the condition under which they fail.
minor comments (4)
  1. [Abstract and §4.4] The abstract states 'preserving resonant transport up to 1 MGy with about 83 percent peak-current retention'; Section 4.4 reports a 'peak current reduction at 1 MGy: ∼17%' for the screened case. These two statements are consistent (83% retention = 17% drop), but the abstract does not mention that this value depends on the undocumented reduced transport functions; adding a qualifier such as 'within the calibrated analytical model' would improve accuracy.
  2. [§3.1 and Supplementary Material S6] The Supplementary Material text in Fig. S1 refers to 'Eq. (6) of the main manuscript' for the transmission calculation, but the transmission function is defined by Eq. (8) of the main text, while Eq. (6) is the screening factor. This is a typographical cross-reference error that should be corrected.
  3. [§3.4 and Table 1] The sub-picosecond RC time is computed with R_ch ≈ 1.2 kΩ, which is described as an order-of-magnitude estimate; the paper notes this is a low-temperature reference value. This is acceptable for a design framework, but Table 1 should state that τ_int is directly proportional to R_ch and is therefore an estimate rather than a device-specific prediction.
  4. [§4.8 and Eqs. (S9)–(S10)] The robustness bounds for PVR and J_p are obtained using phenomenological scaling relations (S9)–(S10) with coefficients (0.8, 1.0) chosen as illustrative. The text does report the extremal bounds, but the reader cannot tell how much of the PVR variation (e.g., −37%/+58% for D_it) comes from the transport model versus these illustrative coefficients. A sentence stating that the PVR/J_p bounds are illustrative scaling outcomes rather than Landauer results would clarify the status of the robustness claims.

Circularity Check

2 steps flagged · score 6.0 of 10

The headline 1 MGy transport metrics (PVR~10, ~83% retention, ~17% current drop) are generated by reduced transport functions calibrated to reference points taken from the very tables that report them, making those predictions partially circular; the unscreened shift coefficient is additionally imported from the authors' own prior preprint.

  1. fitted input called prediction [Section 4.7; Supplementary Software descriptions (parameters.py, transport.py, electrostatics.py)]
    "In the actual implementation, the peak-to-valley ratio and the normalised peak current are obtained by evaluating the reduced transport functions PVR(ΔE) and Inorm(ΔE) over the dose-dependent shift ΔE(D,η), as implemented in the pvr_from_deltaE() and inorm_from_deltaE() routines. ... parameters.py: stores all model parameters (characteristic dose D0 =500 Gy, unscreened ΔEmax(0)=70 meV shift, calibration constants for the reduced transport functions, and reference points taken from Tables 3 and 4)."

    The manuscript's headline numbers (PVR≈10, ~83% peak-current retention, ~17% current drop at 1 MGy) are values of PVR(ΔE) and Inorm(ΔE), which are empirical interpolation functions calibrated against reference points taken from Tables 3 and 4—the very tables that report those headline numbers. The functions and the Landauer calibration points are not displayed, so agreement at the screened shift ΔE≈23 meV is enforced by calibration rather than computed from the Landauer integral in Eq. (7). In addition, electrostatics.py feeds these functions with the rational saturation ΔE(D,η)=ΔEmax(η)D/(D+D0), whereas Section 4.7 declares the exponential law (Eqs.

  2. self citation load bearing [Section 3.1, low-dose shift coefficient paragraph; references [5], [17], [18]]
    "The coefficient α_i =0.14 meV/mGy, extracted with an estimated numerical uncertainty of approximately ±0.01 meV/mGy from the convergence criteria of the NEGF–Poisson simulations of the unscreened MR-DWELL structure [5]."

    All screened quantitative outputs, including αeff=α/(1+η), ΔEmax(η)=70/(1+η), the 23 meV asymptotic shift, and the transport metrics, are linear scalings of α_i and ΔEmax(0)=70 meV imported from the authors' own SSRN preprint [5] and companion methodological papers [17,18]. These citations are not machine-checked, code-reproduced within the present submission, or independently falsified, so the numerical content of the radiation-tolerance claims reduces to an unverified self-citation chain. The capacitive-divider screening formula itself is a legitimate derived model, so this step is load-bearing reliance rather than definitional circularity.

full rationale

The electrostatic screening relation of Eq. (13), αeff=α/(1+η), and its companion ΔEmax(η)=ΔEmax(0)/(1+η) are derived from a stated lumped-capacitance model, so substituting η=2.09 to obtain a threefold reduction is a model evaluation, not circularity by itself. The circularity is concentrated in the headline transport predictions: PVR and normalised peak current are obtained from reduced transport functions that are calibrated against reference points taken from Tables 3 and 4, the very tables presenting the reported performance values, and the code implements a rational saturation law for the dose dependence while the main text identifies the exponential law as the genuine physical chain. The unscreened shift coefficient and saturation amplitude are also inherited from the authors' own prior preprint rather than from independent data or an independently verified computation. The paper is transparent that the framework is theoretical and requires experimental validation, and the FLASH and PCA/LDA sections are explicitly disclaimed as conceptual, which keeps the score below 8; nonetheless, the central 1 MGy performance claims partially reduce to calibration anchors, giving a circularity score of 6.

Assumptions & free parameters 9 free parameters · 6 assumptions · 0 invented entities

The ledger shows that all quantitative predictions, factor-three reduction, 23 meV shift, 17 percent current drop, PVR about 10, and 0.28 ps, derive from a small set of fitted or chosen parameters and from interpolation functions calibrated to the same model. No new physical entities are introduced; the fast, slow, and deep trap populations are standard phenomenological categories.

free parameters (9)
  • Unscreened low-dose shift coefficient alpha_1 = 0.14 meV/mGy (+/- 0.01)
    Calibrated from companion NEGF-Poisson simulations [5]; all screened coefficients are alpha_eff = alpha_1/(1+eta), so every dose-shift prediction scales from this fitted number.
  • Characteristic saturation dose D0 = 500 Gy
    Phenomenological parameter in the exponential trap-filling law, Eq. (9); shared by resonance shift and broadening. It sets where saturation begins and is not derived from experimental data.
  • Unscreened asymptotic resonance shift DeltaE_max(0) = 70 meV
    Used as the high-dose limit for resonance 1; screened shifts are DeltaE_max(eta) = 70/(1+eta), so the headline 23 meV is a rearrangement of this assumed value.
  • Saturation broadening amplitudes DeltaGamma_1,max and DeltaGamma_2,max = 0.40 and 0.25 meV
    Set through beta*D0 with beta = 0.8 and 0.5 meV/kGy; no independent measurement supports these values.
  • Graphene quantum capacitance CQ = 0.72 fF per emitter (7.2 uF/cm^2)
    Held fixed at EF about 0.3 eV; the authors state full gate-voltage dependence is reserved for future work, so eta and all screening factors are tied to this fixed value.
  • Reduced transport functions PVR(DeltaE) and Inorm(DeltaE) = Calibration coefficients not disclosed
    Described as reduced models calibrated to reference Landauer points in Section 4.7 and Supplementary S6; headline peak-current retention and PVR are outputs of these fits.
  • Channel resistance Rch = about 1.2 kOhm
    Order-of-magnitude estimate used to compute tau_RC about 0.28 ps; authors note it is a low-temperature reference value.
  • Fast-trap parameters Dfast and tau_fast = 50 Gy, 10 ms
    Illustrative values chosen so the critical dose rate falls in the FLASH range; no data support them.
  • Phenomenological sensitivity coefficients in Eqs. (S9)-(S10) = 0.8 and 1.0
    Chosen to illustrate CQ sensitivity of Jp and PVR; authors explicitly say they are not physical constants.
assumptions (6)
  • domain assumption Trapped charge follows first-order exponential saturation, Qtrap(D) = Qmax[1 - exp(-D/D0)].
    Eq. (9); all dose dependencies derive from this assumed kinetics, with D0 = 500 Gy applied to both shift and broadening.
  • domain assumption Resonance shift and linewidth broadening are linearly proportional to trapped charge and share the same saturation dose.
    Eqs. (10)-(11) and (S11)-(S12); no microscopic justification is given for the linear coupling or the shared D0.
  • domain assumption The graphene/h-BN stack acts as a static series capacitor network with constant CQ.
    Eqs. (1)-(6) and Section 3.2; neglects Fermi-level shifts, disorder puddles, and nonuniform trapped charge under irradiation.
  • domain assumption Coherent Breit-Wigner/Landauer transport with fixed resonance parameters from the literature.
    Eqs. (7)-(8); uses E10, E20, Gamma10, Gamma20 from DWELL measurements [23,24] and ignores inelastic scattering beyond the empirical broadening fit.
  • domain assumption Reduced transport functions PVR(DeltaE) and Inorm(DeltaE) faithfully reproduce Landauer reference results.
    Section 4.7 and Supplementary S6; these are calibrated interpolation functions, not direct Landauer integrals for the main figures.
  • domain assumption The low-dose shift coefficient alpha_1 = 0.14 meV/mGy from companion NEGF-Poisson work is reliable.
    Section 3.1; the calibration is cited from [5], and the solver verification is stated as 'not shown' in Section 4.

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Cite this review

Pith. "Pith review of Electrostatic Interface Engineering with Graphene for Radiation-Tolerant MR-DWELL Dosimeters: A Design Framework." pith.science (2026). https://pith.science/paper/BRXH34OY

@misc{pith2026260806616,
  author       = {Pith},
  title        = {Pith review of: Electrostatic Interface Engineering with Graphene for Radiation-Tolerant MR-DWELL Dosimeters: A Design Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BRXH34OY}},
  note         = {Machine review of arXiv:2608.06616}
}
read the original abstract

We present a graphene-assisted MR-DWELL dosimeter governed by electrostatic interface engineering. By adjusting the h-BN spacer, the ratio between graphene's quantum capacitance and the geometric capacitance tunes the screening factor, suppressing radiation-induced resonance shifts by a factor of three and preserving resonant transport up to 1 MGy with about 83 percent peak-current retention and a PVR of about 10. The intrinsic RC response is sub-picosecond, while second-derivative spectroscopy serves as a practical readout. A three-population trapped-charge framework resolves the steady-state versus transient response, indicating FLASH dose-rate discrimination despite steady-state saturation at 500 Gy. The model provides a rigorous quantitative foundation for designing next-generation radiation-hard resonant-tunneling sensors.

Figures

Figures reproduced from arXiv: 2608.06616 by the authors.

Figure 1
Figure 1. Schematic of the elementary emitter layer sequence, illustrating the vertical DWELL resonant-tunneling structure with the integrated graphene [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Conceptual architecture of the proposed MR-DWELL detector array. (a) Full [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Electrostatic interface engineering principle demonstrating the screening of radiation-induced trapped charge by the graphene quantum capacitance, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Calculated transmission spectra T(E) of the graphene-assisted MR-DWELL structure at accumulated doses of 0, 0.3, 0.6, and 1.0 kGy. Both resonance peaks exhibit a systematic red shift and broadening with increasing dose. The shift gradually saturates at higher doses, co…
Figure 5
Figure 5. Figure 5: Electrostatic design metrics as functions of the capacitance ratio [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Design map of the normalized resonance-energy shift, [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Calculated d 2 I/dV2 spectra of the graphene-assisted MR-DWELL dosimeter (η = 2.09) at accumulated doses of 0, 500, and 1000 Gy. The extrema shift systematically with increasing dose while remaining well resolved, supporting the use of differential-conductance spectros…
Figure 8
Figure 8. Figure 8: Conceptual positioning of the proposed graphene-assisted MR-DWELL dosimeter with respect to conventional radiotherapy (RT), flattening [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.