A precise method for phase and amplitude detection in both the time and frequency domains of terahertz spectroscopy based on the weak-value amplification technique is proposed and demonstrated. Within the weak-value amplification scheme, the imaginary weak value enhances variations in the terahertz phase signals, whereas the real weak value amplifies changes in the terahertz amplitude signals. By employing various postselections in the terahertz weak measurement procedure in detecting minute changes of the phase and amplitude of the terahertz wave, we achieved a phase change range from −0.0187 rad to 0.0183 rad with an interval of 0.004 rad and an amplitude change range from −0.0238 rad to 0.0228 rad with an interval of 0.0056 rad. This results in a phase and amplitude measurement resolution of 10−4 rad in the time domain. In the frequency domain, E $\left\vert E\right\vert $ spectra are calculated to assess phase and amplitude variations with respect to frequency or wavelength. We apply these methods to chiral detection, particularly in measuring optical activity such as circular dichroism (CD) and optical rotatory dispersion (ORD). Despite challenges such as strong terahertz wave absorption in aqueous solutions and weak optical responses from natural chiral materials in the terahertz band, we successfully conducted chiroptical spectroscopy on a relatively large volume (2.3 mL) of liquid (R)- and (S)-limonene, as well as lactose tablets with varying mass fractions. Furthermore, the carrier-envelope phase (CEP) shift, defined for one- or few-cycle time-domain terahertz pulses, was effectively achieved through the manipulation of a pair of terahertz polarizers in the terahertz beam path. Notably, when ϕ CEP = 0, a 150 % increase in the absorption coefficient of lactose was observed when weak measurement techniques were employed, compared to conditions without such measurements. This effort yielded THz-ORD and THz-CD spectra, demonstrating the potential of our methods to overcome traditional limitations and provide new insights into the optical response, dynamic properties, and low-frequency vibrational modes of biomolecules and materials in low-energy states, ultimately facilitating the identification of chiral stereoisomers.

Quartz fiber-reinforced polymer (QFRP) is a vital non-polar material used in aviation wave-transparent structural components. Automatic characterization of delamination defects in QFRP is critical to aviation structural component safety. Terahertz time-domain spectroscopy (THz-TDS) is one of the new non-destructive testing (NDT) methods with highly accurate characterization of internal defects in non-polar material. Hence, attempts to extract features of THz time-domain signals for automatic characterization have been made by using deep learning algorithms. In this work, a Transformer-based neural network to classify the THz time-domain signals collected from a QFRP curved structure for automatic characterization of pre-embedded delamination defects has been reported. A THz-TDS system combined with a collaborative robot for collecting the THz signals from QFRP curved structure has been built. An automatic characterization method framework is developed. Results show that the precision rates of Transformer-based neural network for 1st delamination to 5th delamination are 1.0, 1.0, 1.0, 0.985, 1.0, and F1 score of it is 0.982. During the process of testing, delamination defects inside the QFRP curved structure were visualized using pixels with different colors. Results indicate that the Transformer-based neural network can characterize all pre-embedded delamination defects while minimizing false identification of non-defective areas, performing outstanding generalization.

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.

As an alternative solution to the lack of cost-effective multipixel terahertz cameras, terahertz single-pixel imaging that is free from pixel-by-pixel mechanical scanning has been attracting increasing attention. Such a technique relies on illuminating the object with a series of spatial light patterns and recording with a single-pixel detector for each one of them. This leads to a trade-off between the acquisition time and the image quality, hindering practical applications. Here, we tackle this challenge and demonstrate high-efficiency terahertz single-pixel imaging based on physically enhanced deep learning networks for both pattern generation and image reconstruction. Simulation and experimental results show that this strategy is much more efficient than the classical terahertz single-pixel imaging methods based on Hadamard or Fourier patterns, and can reconstruct high-quality terahertz images with a significantly reduced number of measurements, corresponding to an ultra-low sampling ratio down to 1.56%. The efficiency, robustness and generalization of the developed approach are also experimentally validated using different types of objects and different image resolutions, and clear image reconstruction with a low sampling ratio of 3.12% is demonstrated. The developed method speeds up the terahertz single-pixel imaging while reserving high image quality, and advances its real-time applications in security, industry, and scientific research.

Aggregation‐induced emission luminogens (AIEgens) are widely used as photosensitizers for image‐guided photodynamic therapy (PDT). Due to the limited penetration depth of light in biological tissues, the treatments of deep‐seated tumors by visible‐light‐sensitized aggregation‐induced emission (AIE) photosensitizers are severely hampered. Microwave dynamic therapy attracts much attention because microwave irradiation can penetrate very deep tissues and sensitize the photosensitizers to generate reactive oxygen species (ROS). In this work, a mitochondrial‐targeting AIEgen (DCPy) is integrated with living mitochondria to form a bioactive AIE nanohybrid. This nanohybrid can not only generate ROS under microwave irradiation to induce apoptosis of deep‐seated cancer cells but also reprogram the metabolism pathway of cancer cells through retrieving oxidative phosphorylation (OXPHOS) instead of glycolysis to enhance the efficiency of microwave dynamic therapy. This work demonstrates an effective strategy to integrate synthetic AIEgens and natural living organelles, which would inspire more researchers to develop advanced bioactive nanohybrids for cancer synergistic therapy.

In this article, we demonstrate an efficient terahertz single-pixel imaging system incorporating deep learning networks. Experimental results show that by combining a Hadamard single-pixel imaging system with the deep learning network, the sampling time per pattern can be reduced to 1/20 of the conventional system and the number of Hadamard patterns can be reduced to 10% of the pixels while maintaining high image quality with acceptable signal-to-noise ratio above 20 dB and structural similarity of more than 0.85. We thus expect this article to advance the development of a real-time terahertz single-pixel imaging system and promote its applications.

We report a photoconductor-based terahertz metadevice whose transmission characteristics can be comprehensively controlled by a combination of an optical excitation and electrical bias. A metasurface with interdigitated electrodes is proposed to simultaneously support a terahertz resonance and the photoconductive effect, allowing efficient local and global dual-tuning of the carriers in the semiconductor and thus the terahertz wave, based on the simultaneous external optical and electrical stimuli. Experimental results reveal the opposite tuning trend of optical and electrical excitations, leading to the comprehensive transmission modulation of terahertz wave. Our study demonstrates a proof-of-concept device for sophisticated manipulation of terahertz radiation.

We demonstrate a deep residual network with transfer learning strategy for automatic terahertz (THz) recognition of the hidden defects in fiber reinforced polymer (FRP) composites with small-scale training data. The recognition performance with high accuracy, precision, sensitivity and specificity indicate the effectiveness of the proposed method for automatically identifying different defects in THz nondestructive applications.

太赫兹成像技术具有透视性、安全性以及光谱分辨能力等独特优点,有着广泛的应用前景。由于太赫兹面阵探测器的技术成熟度低、价格昂贵,太赫兹成像技术在较长时间内以单点扫描方案为主,存在系统复杂、成像耗时长等问题。近年来,基于计算成像算法的太赫兹单像素成像技术发展迅速,成为了获取太赫兹图像的重要途径之一。文章综述了太赫兹单像素计算成像技术的基本原理、技术实现手段和应用前景,总结了现存的一些关键问题,并展望了一些今后可能的发展方向。

Most contemporary X‐ray detectors adopt device structures with non/low‐gain energy conversion, such that a fairly thick X‐ray photoconductor or scintillator is required to generate sufficient X‐ray‐induced charges, and thus numerous merits for thin devices, such as mechanical flexibility and high spatial resolution, have to be compromised. This dilemma is overcome by adopting a new high‐gain device concept of a heterojunction X‐ray phototransistor. In contrast to conventional detectors, X‐ray phototransistors allow both electrical gating and photodoping for effective carrier‐density modulation, leading to high photoconductive gain and low noise. As a result, ultrahigh sensitivities of over 10 5 μC Gy air −1 cm −2 with low detection limit are achieved by just using an ≈50 nm thin photoconductor. The employment of ultrathin photoconductors also endows the detectors with superior flexibility and high imaging resolution. This concept offers great promise in realizing well‐balanced detection performance, mechanical flexibility, integration, and cost for next‐generation X‐ray detectors.

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

We demonstrate terahertz single-pixel imaging is improved by using a photomodulator based on silicon passivated with SiO 2 . By exploring various SiO 2 thicknesses, we show that the modulation factor of the as-fabricated terahertz photomodulator can reach 0.9, three times that based on bare silicon. This improvement originates from chemical passivation, as well as anti-reflection. Single-pixel imaging experiments based on the compressed sensing method show that reconstructed images adopting the new photomodulator have better quality than the conventional terahertz modulator based on bare silicon. Since the passivation process is routine and low cost, we expect this work will reduce the cost of terahertz photomodulator and single-pixel THz imaging, and advance their applications.

We demonstrate Fourier single-pixel imaging in the terahertz regime. The experimental system is implemented with a photo-induced coded aperture setup, where a monolayer graphene on a high-resistance silicon substrate illuminated by a coded laser beam works as a terahertz modulator. Results show that high-quality terahertz images can be reconstructed using greatly reduced number of measurements. We further find that deep photo-induced terahertz modulation by adding a monolayer graphene on the silicon substrate and by using high laser power can significantly improve the image quality. Compared to Hadamard single-pixel imaging with re-ordered Hadamard matrix, the Fourier approach has higher image quality. We expect that this work will speed up the efficiency of single-pixel terahertz imaging and advance terahertz imaging applications.

We numerically investigate the thermal effects in a cornea illuminated by terahertz radiation. By modifying the bioheat and Arrhenius equations, we studied the heat-transfer and temperature distributions in the corneal tissue, and evaluated the potential thermal damage. The influence of the beam radius and power density are discussed. We also estimated the effective cornea-collagen shrinkage region, and evaluated the degree of thermal damage in the cornea. We expect this work to open up a novel effective and safe thermal-treatment approach based on THz radiation for cornea reshaping in the field of ophthalmology.

We propose and demonstrate a low-cost single-pixel terahertz imaging method based on near-field photomodulation and compressed sensing. By using monolayer graphene on a silicon substrate as the photomodulator, and a low-cost continuous-wave laser and digital micromirror device for effective patterned photomodulation, we achieve fast single-pixel terahertz imaging based on the compressed sensing algorithm. We further show that adopting a graphene on silicon substrate leads to deeper modulation depth and thus better image quality than a high-resistance silicon substrate. We expect this work will advance the development of low-cost single-pixel terahertz imaging and promote this technique into practical applications.

Here, we propose an effective classification strategy for THz pulsed signals of breast tissues based on wavelet packet energy (WPE) feature exaction and machine learning classifiers. The parafin-embedded breast tissue samples were adopted in this study and identified as tumor (226 samples), healthy fibrous tissue (233 samples) or adipose tissue (178 samples) based on the histological results. Firstly, the THz pulsed signals of tissue samples were acquired using a standard transmission THz time-domain spectrometer. Then, the signals were decomposed by the wavelet packet transform (WPT) and the features of the WPE were extracted. To reduce the dimensionality of extracted features, the principal components analysis (PCA) method was employed. Six different machine learning classifiers were then performed and compared for automatic classification of different tissue samples. The highest classification accuracy is up to 97% using the fine Gaussian support vector machine (SVM) approach. The results indicate that the WPE feature exaction combined with machine learning classifier can be used for automatic evaluation of biological tissue THz signals with good accuracy.

The purpose of the present study was to explore the efficacy of arthroscopic loose body removal for knee osteoarthritis (KOA). A total of 23 patients with KOA were enrolled and randomly received conservative treatment (conservative group; n=10) or loose body removal surgery (surgery group; n=13). The serum levels of disease activity indices, including hypersensitive C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR) and synovial inflammatory factors [interleukin (IL)-1 and IL-6] were detected prior to surgery, and at 4 days, 2 or 4 weeks after surgery. All patients were followed up for 2 years and the cure rate was estimated. No significant difference was identified in pre-operative plasma levels of hs-CRP and ESR as well as the synovial concentration of IL-1 and IL-6 between the two groups (all P>0.05). At 2 and 4 weeks after treatment, the levels of these parameters in the surgery group were significantly lower than those in the conservative group (all P<0.05), although the maximum value of these parameters was higher in the surgery group than in the conservative group at 4 days after surgery. The cure rate for KOA in the surgery group was significantly higher than that in the conservative group. In conclusion, the results demonstrated that arthroscopic loose body removal is a more effective treatment than conservative therapy for KOA.

We show that ultrasensitive THz sensors can be achieved based on corrugated hyperbolic metamaterials. For the proposed sensor the sensitivity of the proposed sensor reaches 1 THz/RIU (9.3 × 10 3 nmlRIU), which is orders of magnitude larger than those based on metasurfaces.

Waveguides made of hyperbolic metamaterials have unique properties such as extremely high field enhancement. Here we show that hyperbolic slot waveguides, i.e., nanoscale plasmonic slot waveguides cladded by hyperbolic metamaterials can have greatly enhanced Kerr nonlinear effects at even shorter device lengths and lower pump power compared with the conventional metallic slot waveguides. This result points to novel nanoscale waveguide designs for achieving low-driving-power and compact nonlinear nanophotonic devices that are used in diverse applications at both classical and quantum levels.