This paper reviews recent advances in the design and performance of our original InP- and GaAs-based plasmonic high-electron-mobility transistors (HEMTs) for ultrahighly-sensitive terahertz (THz) sensing and imaging. First, the fundamental theory of plasmonic THz detection is briefly described. Second, single-gate HEMTs with parasitic antennae are introduced as a basic core device structure, and their detection characteristics and sub-THz imaging potentialities are investigated. Third, dual-grating-gate (DGG)-HEMT structures are investigated for broadband highly sensitive detection of THz radiations, and the record sensitivity and the highly-sensitive THz imaging are demonstrated using the InP-based asymmetric DGG-HEMTs. Finally, the obtained results are summarized and future trends are addressed

The imaging diagnosis and prognostication of different degrees of traumatic brain injury (TBI) is very important for early care and clinical treatment. Especially, the exact recognition of mild TBI is the bottleneck for current label-free imaging technologies in neurosurgery. Here, we report an automatic evaluation method for TBI recognition with terahertz (THz) continuous-wave (CW) transmission imaging based on machine learning (ML). We propose a new feature extraction method for biological THz images combined with the transmittance distribution features in spatial domain and statistical distribution features in normalized gray histogram. Based on the extracted feature database, ML algorithms are performed for the classification of different degrees of TBI by feature selection and parameter optimization. The highest classification accuracy is up to 87.5%. The area under the curve (AUC) scores of the receiver operating characteristics (ROC) curve are all higher than 0.9, which shows this evaluation method has a good generalization ability. Furthermore, the excellent performance of the proposed system in the recognition of mild TBI is analyzed by different methodological parameters and diagnostic criteria. The system can be extensible to various diseases and will be a powerful tool in automatic biomedical diagnostics.

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We demonstrated that in vivo brain glioma in a mouse model using a continuouswave terahertz reflection imaging system, as well as the ex vivo fresh brain tissues in mouse model. The tumor regions of in vivo and ex vivo brain tissues can be well distinguished by THz intensity imaging at the frequency of 2.52THz. The THz images with high sensitivity correlated well with magnetic resonance, visual and hematoxylin and eosin stained images. Furthermore, the THz spectral difference between brain gliomas and normal brain tissues were obtained in the 0.6THz to 2.8THz range, where brain gliomas have the higher refractive indices and absorption coefficients, and their differences increase particularly in the high frequency range. These results suggest that THz imaging has great potential as an alternative method for the intraoperative label-free diagnosis of brain glioma in vivo.

In almost any branch of chemistry or life sciences, it is often necessary to study the interaction between different components in a system by varying their respective concentrations in a systematic manner. Currently, many procedures for generating a series of samples of different solute concentration levels are still done manually by dilution. To address this issue, we present herein a highly automated linear concentration gradient generator based on centrifugal microfluidics. The operation of this device is based on the use of multi-layered microfluidics in which individual fluidic samples to be mixed together are stored and metered in their respective layers before finally being transferred to a mixing chamber. To demonstrate the operation of this scheme, we have used the device to conduct antimicrobial susceptibility testing (AST). Firstly, DI water, ampicillin solution and E. coli suspension were loaded into the chambers in different layers. As the device went through several rounds of spinning at different speeds, a series of metered dosages of ampicillin along a linear concentration gradient were introduced to the mixing chamber and mixed with E. coli automatically. By monitoring the spectral absorbance of the suspensions, we were able to establish the minimum inhibitory concentration (MIC) value of ampicillin against E. coli. The process took about 3 hours to complete, and the experimental results showed a strong correlation with those obtained with the standard CLSI broth dilution method. Clearly, the platform is useful for a wide range of applications such as drug discovery and personalised medicine, where concentration gradients are of concern.

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