Quantum Tunnelling-Integrated Optoplasmonic Nanotrap

• QTOP-trap is an integrated optoelectronic platform that combines plasmonic optical trapping with quantum tunnelling to enable label-free, single-molecule protein conductance detection.
• It uses a dual-barrelled quartz nanopipette with gold nanoelectrodes to create a tunable tunnelling gap, achieving sub-3 nm spatial resolution and 10 μs temporal precision.
• The device quantifies dynamic electron transfer in proteins through real-time conductance tracking, offering new insights into non-equilibrium biochemical processes.

The Quantum Tunnelling-Integrated Optoplasmonic Nanotrap (QTOP-trap) is an optoelectronic platform that integrates plasmonic optical trapping with real-time quantum tunnelling conductance measurements. This technology enables label-free, single-molecule resolution of protein conductance in physiological electrolyte environments, combining sub-3 nm spatial precision with 10 μs temporal and sub-pA current noise sensitivity. QTOP-trap provides a universal framework for dissecting non-equilibrium electron transfer (ET) mechanisms in dynamic, conformationally active proteins, directly correlating tertiary structure dynamics with electron conductance through synchronised optoelectronic measurements (Zeng et al., 4 Jan 2026).

1. Device Architecture and Physical Principles

QTOP-trap utilizes a double-barrelled quartz nanopipette with a tip diameter of approximately 200 nm, fabricated by laser pulling and internal carbon deposition/etching. Gold nanoelectrodes are formed on each barrel via electrodeposition, producing a tunable tunnelling gap ($d \approx 0.5$–3 nm). Protein-specific functionalization is achieved via gold–thiol chemistry, such as cysteine, NTA–Cu²⁺, or biotin–streptavidin coupling, establishing a robust experimental platform for single-molecule studies.

Under dark-field illumination, the tip exhibits a broad plasmon resonance at 583–751 nm, corroborated by FDTD simulations that model the gap as two 50 nm Au spheres separated by 0.5–3 nm, incorporating 5 nm surface asperities to replicate the spectral response.

The plasmonic optical trap is energized by a 637 nm laser diode, focused through a 40×, NA 0.6 objective, yielding power densities up to 1 mW/μm². Linear polarization is aligned with the electrode gap axis, achieving local near-field enhancement $|E|^2/|E_0|^2 > 10^6$ at the gap centre for $d = 0.5$ nm. Optical trapping relies on field gradient forces: $$F_{\text{grad}} = \frac{1}{2}\text{Re}[\alpha] \nabla |E(r)|^2,$$ with the optical potential: $$U_{\text{opt}}(r) = -\frac{1}{2}\text{Re}[\alpha]|E(r)|^2,$$ where $\alpha$ is the molecular polarizability. The depth of this potential well reaches $U_{\text{opt,max}} \approx 1.1 k_BT_0$ at 300 K, with maximum gradient forces up to 11 pN, efficiently trapping individual target molecules.

When a molecule bridges the nanoelectrodes, quantum tunnelling current is detected and characterized by a non-ohmic Simmons model: $$I(V) = \frac{Ae}{2\pi h d^2}\bigl[(\phi - \tfrac{eV}{2})\exp(-2d\sqrt{\tfrac{2m(\phi - eV/2)}{\hbar^2}}) - (\phi + \tfrac{eV}{2})\exp(-2d\sqrt{\tfrac{2m(\phi + eV/2)}{\hbar^2}})\bigr],$$ with empirical forms $I(V) \propto V\,\exp[-2\kappa d]$, $\kappa = \sqrt{2m\phi/\hbar^2}$ for small-bias, symmetric barriers. Barrier height $\phi$ (0.5–3.5 eV) and gap $d$ are fitted per experiment.

2. Instrumentation and Measurement Protocols

The optoplasmonic platform is constructed around a combination of optical, fluidic, and electronic control elements. It employs a laser (637 nm, CW or modulated at $\sim$1.077 kHz), a high-NA objective for laser focusing and alignment, a Zurich MFLI lock-in amplifier for photocurrent demodulation (reference set to laser modulation), and a MultiClamp 2400 voltage-clamp amplifier for high-precision tunnelling current recording, digitized at 100 kHz bandwidth (10 μs temporal resolution). The solution environment is maintained in a quartz flow cell (1.5 mm ID) to permit in-solution experiments under physiological conditions.

Operationally, optical trap alignment is optimised by demodulating the photothermal response via lock-in methods; once aligned, the system records continuous DC tunnelling currents at the maximum sampling rate. Synchronization between laser modulation (via TTL shutter controls) and current traces ensures rigorous time-stamped correlation between optical excitation and conductance events.

Spatial calibration is achieved by scanning the tip in XYZ (25 nm steps), producing a sub-50 nm spatially localized hotspot; geometric constraints limit protein capture to a $<3$ nm region. Temporal fidelity is calibrated by step-function injection and amplifier response characterization.

3. Data Processing and Quantitative Analysis

Raw current traces $(I(t))$ regularly present baseline drift driven by thermal and ionic noise. To address this, Asymmetric Least Squares (ALS) smoothing is used: $$\min_z \sum_i w_i(y_i - z_i)^2 + \lambda\sum_i (z_{i-1} - 2z_i + z_{i+1})^2,$$ where $w_i$ depend on the residual sign.

Peaks are detected using two thresholds: $$\text{Th}_1 = z + S_1 \cdot \sigma, \qquad \text{Th}_2 = z + S_2 \cdot \sigma,$$ where $\sigma$ is the estimated baseline noise. Only events surpassing $\text{Th}_2$ in amplitude and exceeding a 10 μs duration are retained.

Each tunnelling event is characterized by amplitude ($\Delta I$) and dwell time ($\tau$). Conductance increments are calculated as $\Delta G = \Delta I/V_\text{bias}$. Dwell-time statistics conform to a single-exponential distribution $P(\tau) \propto \exp(-\tau/\tau_0)$, and two-dimensional density plots in $(\Delta G, \log\tau)$ space serve to identify molecule-specific or mutant fingerprints. K-means clustering on $\Delta G$ histograms supports robust discrimination of protein variants, with validation via Calinski–Harabasz and Silhouette indices.

4. Tethered Protein Switch and Real-Time Conformational Tracking

To facilitate real-time junction stability and repeated kinetic measurement, proteins are site-specifically tethered to one electrode. Strategies include His₆–Cu²⁺–NTA and biotin–streptavidin (via MSA on biotin-PEG thiol–modified gold). This molecular anchoring enables repeated protein–junction formation and release upon laser capture sequences.

Real-time conformational analysis is exemplified with His₆-tagged Hsp90. Under trapping conditions (7 mW, 100 mV bias), Hsp90 forms a stable conductive junction ($I \sim 1$–2 nA). Upon ATP (1 mM) addition, the conductance fluctuates stochastically between two discrete states, corresponding to the protein’s open ($G_1 = 423.9$ nS) and closed ($G_2 = 514.7$ nS) conformations. The dwell-time distributions for each state yield transition rates $k_{1\to2}$ and $k_{2\to1}$ via: $$P(\tau_{1\to2}) = k_{1\to2} \exp(-k_{1\to2} \tau), \qquad P(\tau_{2\to1}) = k_{2\to1} \exp(-k_{2\to1} \tau).$$ Extracted rates $k_{1\to2} = 0.88 \pm 0.02$ ms$^{-1}$ and $k_{2\to1} = 2.26 \pm 0.06$ ms$^{-1}$ are congruent with established ATPase kinetics for Hsp90, demonstrating molecular-state-resolved, real-time conductance tracking.

5. Performance Metrics

The QTOP-trap achieves:

Metric	Typical Value	Determination Method
Spatial resolution	$<$3 nm	FDTD & photocurrent mapping
Temporal resolution	10 μs	Digitizer (100 kHz), step-function cal.
Conductance sensitivity	1–10 pS ($\Delta G$)	Baseline sub-pA noise, pA–nA range
Dynamic range	$0.06–1.5$ μS (single-protein $\Delta G$)	Experimental data, Table S1
Optical trap force	up to 11 pN	FDTD, DFT-calculated $\alpha$

AC photocurrent interference remains below 1% of the DC tunnelling signal at 200 mV bias, ensuring high signal-to-noise for continuous trapping and measurement.

6. Applications and Research Significance

QTOP-trap directly links quantum mechanical tunnelling events with protein conformational dynamics in solution, delivering mechanistic insight into nonequilibrium ET at the single-molecule level. Applications include:

• Single-molecule enzymology and identification of protein variants under physiological conditions.
• Real-time observation of protein folding/unfolding and dynamic mapping of electron-transfer pathways.
• Mechanistic dissection of membrane protein ET and photosynthetic complexes.
• Rational design and conductance benchmarking of protein-based quantum devices.

This approach obviates the limitations of ensemble averaging and non-physiological constraints, allowing direct visualization and quantification of the quantum mechanical underpinnings of bioenergetic processes (Zeng et al., 4 Jan 2026).

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References

• Zeng et al., "Quantum tunnelling-integrated optoplasmonic nanotrap enables conductance visualisation of individual proteins" (Zeng et al., 4 Jan 2026).