Abstract-THz Detection of Biomolecules in Aqueous Environments—Status and Perspectives for Analysis Under Physiological Conditions and Clinical use

Christian Weisenstein, Anna Katharina Wigger, Merle Richter, Robert Sczech, Anja Katrin Bosserhoff, Peter Haring Bolívar,  

https://link.springer.com/article/10.1007/s10762-021-00792-9

Bioanalytical THz sensing techniques have proven to be an interesting and viable tool for the label-free detection and analysis of biomolecules. However, a major challenge for THz bioanalytics is to perform investigations in the native aqueous environments of the analytes. This review recapitulates the status and future requirements for establishing THz biosensing as a complementary toolbox in the repertoire of standard bioanalytic methods. The potential use in medical research and clinical diagnosis is discussed. Under these considerations, this article presents a comprehensive categorization of biochemically relevant analytes that have been investigated by THz sensing techniques in aqueous media. The detectable concentration levels of ions, carbohydrates, (poly-)nucleotides, active agents, proteins and different biomacromolecules from THz experiments are compared to characteristic physiological concentrations and lower detection limits of state-of-the-art bioanalytical methods. Finally, recent experimental developments and achievements are discussed, which potentially pave the way for THz analysis of biomolecules under clinically relevant conditions.

Abstract-Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction

Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar, Andreas Kolb, Michael Möller

https://arxiv.org/abs/1907.01377

Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.

Abstract-Computational Image Enhancement for Frequency Modulated Continuous Wave (FMCW) THz Image

Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar, Andreas Kolb

Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar, Andreas Kolb

In this paper, a novel method to enhance Frequency Modulated Continuous Wave (FMCW) THz imaging resolution beyond its diffraction limit is proposed. Our method comprises two stages. Firstly, we reconstruct the signal in depth-direction using a sinc-envelope, yielding a significant improvement in depth estimation and signal parameter extraction. The resulting high-precision depth estimate is used to deduce an accurate reflection intensity THz image. This image is fed in the second stage of our method to a 2D blind deconvolution procedure, adopted to enhance the lateral THz image resolution beyond the diffraction limit. Experimental data acquired with a FMCW system operating at 577 GHz with a bandwidth of 126 GHz shows that the proposed method enhances the lateral resolution by a factor of 2.29 to 346.2 μm with respect to the diffraction limit. The depth accuracy is 91 μm. Interestingly, the lateral resolution enhancement achieved with this blind deconvolution concept leads to better results in comparison with conventional gaussian deconvolution. Experimental data on a PCB resolution target is presented, in order to quantify the resolution enhancement and to compare the performance with established image enhancement approaches. The presented technique allows exposure of the interwoven fiber reinforced embedded structures of the PCB test sample.

Abstract-Terahertz Dielectric Resonator Antenna Coupled to Graphene Plasmonic Dipole

Seyed E. Hosseininejad, Mohammad Neshat, Reza Faraji-Dana, Sergi Abadal, Max C. Lemme, Peter Haring Bolívar, Eduard Alarcón, Albert Cabellos-Aparicio

https://arxiv.org/abs/1801.03834

This paper presents an efficient approach for exciting a dielectric resonator antenna (DRA) in the terahertz frequencies by means of a graphene plasmonic dipole. Design and analysis are performed in two steps. First, the propagation properties of hybrid plasmonic onedimensional and two-dimensional structures are obtained by using transfer matrix theory and the finite-element method. The coupling amount between the plasmonic graphene mode and the dielectric wave mode is explored based on different parameters. These results, together with DRA and plasmonic antenna theory, are then used to design a DRA antenna that supports the TE112y mode at 2.4 THz and achieves a gain (IEEE) of up to 7 dBi and a radiation efficiency of up 70%. This gain is 6.5 dB higher than that of the graphene dipole alone and achieved with a moderate area overhead, demonstrating the value of the proposed structure.