Numerical investigation of a graphene-on-semiconductor device for optical monitoring of cell electrophysiology.

Spatially resolved sensing devices for electrostatic potentials are extremely useful for characterization of living cells, however, many current techniques lack the speed necessary to capture spatially resolved, functional information of cells in real-time. Here, an optical sensing technique is proposed based on graphene on a semiconductor stack operating in the near-infrared spectrum. By modeling coherent interference of multiply reflected beam paths within the semiconductor stack, we demonstrate how the device produces a continuous reflectivity change in response to graphene Fermi energy which is ideal for sensing changes in local electrostatic fields produced by action potentials of living cells. By coupling the device with a high-speed camera, we propose this platform will allow for high-speed imaging of action potentials over a large sensing area with micron scale resolution.

Photoelectrochemical imaging of single cardiomyocytes and monitoring of their action potentials through contact force manipulation of organoids.

Accurate monitoring of cardiomyocyte action potentials (APs) is essential to understand disease propagation and for trials of novel therapeutics. Patch clamp techniques offer 'gold standard' measurements in this field, but are notoriously difficult to operate and only provide measurements of a single cell. Here we propose photoelectrochemical imaging (PEI) with light-addressable potentiometric sensors (LAPS) in conjunction with a setup for controlling the contact force between the cardiomyocyte organoids and the sensor surface for measuring APs with high sensitivity. The method was validated through measuring the responses to drugs, and the results successfully visualized the expected electrophysiological changes to the APs. PEI allows for several cells to be monitored simultaneously, opening further research to the electrophysiological interactions of adjoining cells. This method expands the applications of PEI to three-dimensional geometries and provides the fields of stem cell research, drug trials and heart disease modelling with an invaluable tool to further investigate the role of APs.

Deep neural network ensembles for THz-TDS refractive index extraction exhibiting resilience to experimental and analytical errors.

Terahertz time-domain spectroscopy (THz-TDS) achieves excellent signal-to-noise ratios by measuring the amplitude of the electric field in the time-domain, resulting in the full, complex, frequency-domain information of materials' optical parameters, such as the refractive index. However the data extraction process is non-trivial and standardization of practices are still yet to be cemented in the field leading to significant variation in sample measurements. One such contribution is low frequency noise offsetting the phase reconstruction of the Fourier transformed signal. Additionally, experimental errors such as fluctuations in the power of the laser driving the spectrometer (laser drift) can heavily contribute to erroneous measurements if not accounted for. We show that ensembles of deep neural networks trained with synthetic data extract the frequency-dependent complex refractive index, whereby required fitting steps are automated and show resilience to phase unwrapping variations and laser drift. We show that training with synthetic data allows for flexibility in the functionality of networks yet the produced ensemble supersedes current extraction techniques.

Coherent waveguide laser arrays in semiconductor quantum well membranes.

Coherent laser arrays compatible with silicon photonics are demonstrated in a waveguide geometry in epitaxially grown semiconductor membrane quantum well lasers transferred on substrates of silicon carbide and oxidised silicon; we record lasing thresholds as low as 60 mW of pump power. We study the emission of single lasers and arrays of lasers in the sub-mm range. We are able to create waveguide laser arrays with modal widths of approximately 5 - 10 µm separated by 10 - 20 µm, using real and reciprocal space imaging we study their emission characteristics and find that they maintain their mutual coherence while operating on either single or multiple longitudinal modes per lasing cavity.

Artificial neural networks for material parameter extraction in terahertz time-domain spectroscopy.

Terahertz time-domain spectroscopy (THz-TDS) is a proven technique whereby the complex refractive indices of materials can be obtained without requiring the use of the Kramers-Kronig relations, as phase and amplitude information can be extracted from the measurement. However, manual pre-processing of the data is still required and the material parameters require iterative fitting, resulting in complexity, loss of accuracy and inconsistencies between measurements. Alternatively approximations can be used to enable analytical extraction but with a considerable sacrifice of accuracy. We investigate the use of machine learning techniques for interpreting spectroscopic THz-TDS data by training with large data sets of simulated light-matter interactions, resulting in a computationally efficient artificial neural network for material parameter extraction. The trained model improves on the accuracy of analytical methods that need approximations while being easier to implement and faster to run than iterative root-finding methods. We envisage neural networks can alleviate many of the common hurdles involved in analyzing THz-TDS data such as phase unwrapping, time domain windowing, slow computation times, and extraction accuracy at the low frequency range.

THz-TDS parameter extraction: empirical correction terms for the analytical transfer function solution.

Terahertz time-domain spectroscopy (TDS) is capable of determining both real and imaginary refractive indices of a wide range of material samples; however, converting the TDS data into complex refractive indices typically involves iterative algorithms that are computationally slow, involve complex analysis steps, and can sometimes lead to non-convergence issues. To avoid using iterative algorithms, it is possible to solve the transfer function analytically by assuming the material loss is low; however, this leads to errors in the refractive index values. Here we demonstrate how the errors created by solving the transfer function analytically are largely predictable, and present a set of empirically derived equations to diminish the error associated with this analytical solution by an impressive two to three orders of magnitude. We propose these empirical correction terms are well suited for use in industrial applications such as process monitoring where analysis speed and accuracy are of the utmost importance.

High-precision THz-TDS via self-referenced transmission echo method.

Terahertz time-domain spectroscopy (TDS) is a powerful characterization technique which allows for the frequency-dependent complex refractive index of a sample to be determined. This is achieved by comparing the time-domain of a pulse transmitted through air to a pulse transmitted through a material sample; however, the requirement for an independent reference scan can introduce errors due to laser fluctuations, mechanical drift, and atmospheric absorption. In this paper, we present a method for determining complex refractive index without an air reference, in which the first pulse transmitted through the sample is compared against the "echo", where the internal reflections delay the transmission of the echo pulse. We present a benchmarking experiment in which the echo reference method is compared to the traditional air method, and show that the echo method is able to reduce variation in real refractive index.

Singlemoded THz guidance in bendable TOPAS suspended-core fiber directly drawn from a 3D printer.

Terahertz (THz) technology has witnessed a significant growth in a wide range of applications, including spectroscopy, bio-medical sensing, astronomical and space detection, THz tomography, and non-invasive imaging. Current THz microstructured fibers show a complex fabrication process and their flexibility is severely restricted by the relatively large cross-sections, which turn them into rigid rods. In this paper, we demonstrate a simple and novel method to fabricate low-cost THz microstructured fibers. A cyclic olefin copolymer (TOPAS) suspended-core fiber guiding in the THz is extruded from a structured 3D printer nozzle and directly drawn in a single step process. Spectrograms of broadband THz pulses propagated through different lengths of fiber clearly indicate guidance in the fiber core. Cladding mode stripping allow for the identification of the single mode in the spectrograms and the determination of the average propagation loss (~ 0.11 dB/mm) in the 0.5-1 THz frequency range. This work points towards single step manufacturing of microstructured fibers using a wide variety of materials and geometries using a 3D printer platform.

Optical Gating of Graphene on Photoconductive Fe:LiNbO3.

We demonstrate experimentally nonvolatile, all-optical control of graphene's charge transport properties by virtue of an Fe:LiNbO3 photoconductive substrate. The substrate can register and sustain photoinduced charge distributions which modify locally the electrostatic environment of the graphene monolayer and allow spatial control of graphene resistivity. We present light-induced changes of graphene sheet resistivity as high as ∼370 Ω/sq (∼2.6-fold increase) under spatially nonuniform light illumination. The light-induced modifications in the sheet resistivity are stable at room temperature but can be reversed by uniform illumination or thermal annealing (100 °C for 4 h), thus restoring graphene's electrical properties to their initial, preillumination values. The process can be subsequently repeated by further spatially nonuniform illumination.