The molecular fingerprint sensing technology based on metasurface has unique attraction in the biomedical field. However, in the terahertz (THz) band, existing metasurface designs based on multi-pixel or angle multiplexing usually require more analyte amount or possess a narrower tuning bandwidth. Here, we propose a novel single-pixel graphene metasurface. Based on the synchronous voltage tuning, this metasurface enables ultra-wideband ( $$\sim$$∼ 1.5 THz) fingerprint enhancement sensing of trace analytes, including chiral optical isomers, with a limit of detection (LoD) ≤ 0.64 μg/mm². The enhancement of the fingerprint signal ( $$\sim$$∼ 17.4 dB) originates from the electromagnetically induced transparency (EIT) effect excited by the metasurface, and the ideal overlap between the light field constrained by single-layer graphene (SLG) and ultra-thin analyte. Meanwhile, due to the unique nonlinear enhancement mechanism in graphene tuning, the absorption envelope distortion is inevitable. To solve this problem, a universal fingerprint spectrum inversion model is developed for the first time, and the restoration of standard fingerprints reaches R_max² ≥ 0.99. In addition, the asynchronous voltage tuning of the metasurface provides an opportunity for realizing the dynamic reconfiguration of EIT resonance and the slow light modulation in the broadband range. This work builds a bridge for ultra-wideband THz fingerprint sensing of trace analytes, and has potential applications in active spatial light modulators, slow light devices and dynamic imaging equipments.

Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.

Organic-inorganic hybrid metal halide perovskites (MHPs) have attracted tremendous attention for optoelectronic applications. The long photocarrier lifetime and moderate carrier mobility have been proposed as results of the large polaron formation in MHPs. However, it is challenging to measure the effective mass and carrier scattering parameters of the photogenerated large polarons in the ultrafast carrier recombination dynamics. Here, we show, in a one-step spectroscopic method, that the optical-pump and terahertz-electromagnetic probe (OPTP) technique allows us to access the nature of interplay of photoexcited unbound charge carriers and optical phonons in polycrystalline CH₃NH₃PbI₃ (MAPbI₃) of about 10 μm grain size. Firstly, we demonstrate a direct spectral evidence of the large polarons in polycrystalline MAPbI₃. Using the Drude–Smith–Lorentz model along with the Frӧhlich-type electron-phonon (e-ph) coupling, we determine the effective mass and scattering parameters of photogenerated polaronic carriers. We discover that the resulting moderate polaronic carrier mobility is mainly influenced by the enhanced carrier scattering, rather than the polaron mass enhancement. While, the formation of large polarons in MAPbI₃ polycrystalline grains results in a long charge carrier lifetime at room temperature. Our results provide crucial information about the photo-physics of MAPbI₃ and are indispensable for optoelectronic device development with better performance.

Microscopic biological objects, including biomacromolecules, cells, and micro-organisms, are the basic components of life, with sizes ranging from tens of nanometers to tens of micrometers. Accurate detection for microscopic biological objects is a powerful tool for studying physiological processes and exploring the development process of diseases. It can improve the understanding of immune processes and metabolic processes. For example, multiple protein/molecular markers are used to distinguish the specific phenotypes of microglia, including pro-inflammatory M1 and anti-inflammatory M2,¹ and to study their function and tissue status. The continuous activation of M1 microglia will damage the peripheral nerve cells, which may lead to neurogenerative diseases. The studies based on microglia can provide a new perspective for the study of the mechanism of neuroinflammation. Therefore, at the microlevel, sensitive and accurate biomedical detection techniques are critical to the prediction and the treatment of disease. According to the detection resolution, the detection techniques for microscopic biological objects can be roughly divided into two categories: one with a resolution of the order of micrometers, and the other with a resolution of the order of nanometers. These two types of detection techniques are discussed separately.

For the mixture components analysis, the typical methods include multiple linear regression, partial linear squares and artificial neural network. However, they all need lots of samples and time to improve the recognition accuracy. Here, in this paper, based on the data obtained from terahertz spectroscopy, we proposed an identification method with less sample requirements and less calculation time but higher accuracy. Basing on the wavelet transform, baseline elimination, support vector regression and loop iteration of samples, the specific substance in mixture can be identified effectively. For example, seven substances existing in brain glioma are chosen as the components of mixture, where the key substances used for glioma diagnostic are set as the target substances, and the spectra of mixtures with different mix proportions are served as training data. The average correlation coefficient of identification achieves 99.135% and root mean square error (RMSE) is 0.40%. These results have profound meaning for the eventual practical application of exact qualitative and quantitative identification of components in mixtures.