Significance: Light-field microscopy (LFM) is a scanning-free 3D imaging technique that is useful for observing dynamic biological systems due to its unique capability to capture both spatial and angular information from samples in a single exposure. However, LFM suffers from the spatial-angular information trade-off associated with microlens arrays, and its spatial resolution is usually unsatisfactory for fine-structure imaging. Aim: To overcome this bottleneck, we introduce a deep-learning-based image fusion technique that combines LFM images with Fourier LFM (FLFM) images. The high spatial resolution of FLFM is combined with the dense angular acquisition capability of LFM to improve 3D image reconstruction quality. Approach: The deep learning network was trained with LFM, FLFM, and epipolar plane image data. The proposed neural network employs specialized feature extraction modules for each modality, with a U-Net backbone for 3D reconstruction, and integrates a hierarchical cascade-based result-level fusion strategy to jointly optimize multimodal features. This approach significantly enhances detail preservation and depth recovery in the final output. Results: Results obtained using a publicly available dataset of synthetic tubulins demonstrate that the proposed method outperforms state-of-the-art techniques. Quantitatively, it achieved a peak signal-to-noise ratio (PSNR) of 38.4729 and a structural similarity index measure (SSIM) of 0.9876, significantly outperforming both traditional algorithms and single-modality deep learning approaches. Furthermore, validation on a mouse brain blood vessels dataset confirms the effectiveness of the method in reconstructing biological structures, achieving a PSNR of 35.0548 and an SSIM of 0.8424. Conclusions: We introduce an approach that combines LFM with FLFM, providing an efficient and reliable solution for practical LFM applications. The deep-learning-based framework demonstrates significant potential to simultaneously accelerate imaging acquisition and enhance 3D reconstruction quality, offering further possibilities for computational microscopy.

Fourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors such as noise, optical aberration, and phase wrapping. In this work, we propose a semi-supervised Fourier ptychographic transformer network (SFPT) for improved image reconstruction, which employs a two-stage training approach to enhance the image quality. First, self-supervised learning guided by low-resolution amplitudes and Zernike modes is utilized to recover pupil function. Second, a supervised learning framework with augmented training datasets is applied to further refine reconstruction quality. Moreover, the unwrapped phase is recovered by adjusting the phase distribution range in the augmented training datasets. The effectiveness of the proposed method is validated by using both the simulation and experimental data. This deep-learning-based method has potential applications for imaging thicker biology samples.

The structure of lepidopteran wing scales plays an important role in the regulation of their biological behaviors, making the 3D imaging of them very useful. However, most of the current three-dimensional microscopic methods are usually destructive to the samples. Fourier light-field microscopy has the characteristics of real-time imaging and multi-angle visualization, thus becomes a preferred method to study the three-dimensional structure of structural color scales in lepidopteran insects. In this study, Fourier light-field microscopy was used to compare the imaging of Morpho helenor and Chrysiridia rhipheus under 10X and 50X objective lenses, where the results demonstrated the difference structure of the wing scale between these two species and the corresponding view angle dependent color variations. This study demonstrates the unique advantages and application prospects of Fourier light-field microscopy for 3D imaging of the large-field thick samples

Spin and orbital angular momenta are two of the most fundamental physical quantities that describe the complex dynamic behaviors of optical fields. A strong coupling between these two quantities leads to many intriguing spatial topological phenomena, where one remarkable example is the generation of a helicity-dependent optical vortex that converts spin to orbital degrees of freedom. The spin-to-orbit conversion occurs inherently in lots of optical processes and has attracted increasing attention due to its crucial applications in spin-orbit photonics. However, current researches in this area are mainly focused on the monochromatic optical fields whose temporal properties are naturally neglected. In this work, we demonstrate an intriguing temporal evolution of the spin-to-orbit conversion induced by tightly-focused femtosecond optical fields. The results indicate that the conversion in such a polychromatic focused field obviously depends on time. This temporal effect originates from the superposition of local fields at the focus with different frequencies and is sensitive to the settings of pulse width and central wavelength. This work can provide fundamental insights into the spin-orbit dynamics within ultrafast wave packets, and possesses the potential for applications in spin-controlled manipulations of light.

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique which can perform complex amplitude imaging with both large field of view and high resolution by using a simple microscope setup. Here, we propose a transformer based neural network named as FP-transformer, which takes the low-resolution amplitude (LRA) images as the sequential input and uses self-attention mechanism to compute the relationship among them. The high-resolution FPM complex amplitude reconstruction is the end-to-end output of the FP-transformer. We apply the image library of div2k to generate the FPM LRA images with the physical model, and then perform the training and validation with this dataset containing ground truth. We also perform the validation with the experiment images and it is found that the high-quality FPM complex amplitude image pairs can be obtained. Therefore, the FP-transformer creates a new platform for the FPM deep learning reconstruction, which has the better dependability and adaptability.The code of this work will be available at https://github.com/zhaolin6/FPTransfomer for the sake of reproducibility.

The ability to use the full solar spectrum energy in photocatalytic processes for environmental remediation and energy production has attracted worldwide attention. Efficient harvesting of near-infrared (NIR) photons, especially in the wavelength range beyond 800 nm, is one of the main driving forces in photocatalytic research. The design of appropriate photocatalytic systems for wide-range light-harvesting from the UV to NIR regions is a promising method of maximizing the efficiency of solar energy utilization. This review comprehensively summarizes the recent progress in full-solar-light-driven photocatalytic systems, including several strategies to harness NIR light and thermal energy. The corresponding photocatalytic mechanisms and design of binary or ternary heterogeneous systems are discussed in detail. Moreover, future perspectives and challenges are presented to inspire the development of further innovations in full-solar-light-driven photocatalysis.

Fourier Ptychographic Microscopy (FPM) is a super-resolution microscopy technology, in which a set of low-resolution images containing different frequency components of the sample can be obtained by changing the angle of the light source in this technology, and then the iterative algorithm is used to reconstruct high-resolution intensity and phase information. The reconstruction usually takes a long time and is not suitable for real-time FPM imaging. It has been recognized recently that the potential fast image reconstruction algorithm is the use of deep learning algorithms. We designed a conditional generative adversarial network (cGAN) which has multi-branch input and multi-branch output which can expand the frequency spectrum of the reconstructed image very well. Based on the convolutional neural network (CNN), the brightfield and darkfield images obtained by FPM imaging can be regarded as different image features obtained by different convolutional kernel, and the skip connection of U-net can effectively utilize this information. The brightfield and darkfield images in FPM imaging are input to different branches, which can avoid missing the darkfield signal information. Importantly, the neural network we designed will continue to perform simulation process of FPM imaging from the recovered high-resolution intensity and phase to obtain low-resolution images and make them correspond one-to-one with the input low-resolution images. These corresponded images will enter loss function, making it easier for the neural network to learn relation between the low-resolution images and the high-resolution images. We validated the deep learning algorithm through simulated experimental research on biological cell imaging.

An auto focusing method based on simulated annealing algorithm is presented to recover the respective high resolution images for three different illumination colors, thus to recover the high resolution color image. Simulation modeling as well as the experiments on resolution target and biological slices all demonstrate that the high resolution color image can be recovered even for the microscopic system with serious chromatic aberration, which significantly relaxes the request for high quality microscope, thus expanding the application scope of the Fourier ptychographic microscopy.

For biomedical photoacoustic applications, an ongoing challenge in simultaneous volumetric imaging and spectroscopic analysis arises from ultrasonic detectors lacking high sensitivity to pressure transients over a broad spectral bandwidth. Photoacoustic impulses can be measured on the basis of the ultrafast temporal dynamics and highly sensitive response of surface plasmon polaritons to the refractive index changes. Taking advantage of the ultra-sensitive phase shift of surface plasmons caused by ultrasonic perturbations instead of the reflectivity change [as is the case for traditional surface plasmon resonance (SPR) sensors], a novel SPR sensor based on phase-shifted interrogation was developed for the broadband measurement of photoacoustically induced pressure transients with improved detection sensitivity. Specifically, by encoding the acoustically modulated phase change into time-varying interference intensity, our sensor achieved an almost five-fold sensitivity enhancement (∼98 Pa noise-equivalent pressure) compared with the reflectivity-mode SPR sensing technologies (∼470 Pa) while retaining a broadband acoustic response of ∼174 MHz. Incorporating our sensor into an optical-resolution photoacoustic microscope, we performed label-free imaging of a zebrafish eye in vivo, enabling simultaneous volumetric visualization and spectrally resolved discrimination of anatomical features. This novel sensing technology has potential for advancing biomedical ultrasonic and/or photoacoustic investigations.

The surface plasmon (SP) sensing technique demonstrates high sensitivity and a broad bandwidth of measuring photoacoustic (PA) pressure transients. In this work, we further present a systematic investigation on PA response characteristics of the recently developed SP-based ultrasonic detector, where the ensemble of surface plasmon polaritons (SPPs) at the metal-dielectric interface is approximated as an equivalent acoustic detector. Relying on the intrinsically ultrafast temporal response (∼140 fs) and highly localized evanescent field (optical penetration depth of ∼185 nm) of the SPPs, the SP sensing can respond ultrasounds with the gigahertz frequency band theoretically, which, however, is far higher than the bandwidth in practical PA detection. We reveal that, due to acoustic interference, the finite lateral probing dimension in the SP sensor imposes an ultimate constraint on the accessible ultrasonic cutoff frequency, representing good agreement with the experimental results by acquiring PA impulses from an optically absorbing graphene film using our SP sensor. The theoretical framework enables analyzing the SP response characteristics of ultrasonic/PA pressure transients, which, therefore, offers guidelines for configuring the SP sensor with adequate sensitivity and bandwidths to access various biomedical PA applications, including volumetric imaging and spectroscopic analysis.

Structure Illumination Microscopy (SIM) is a wide-field super-resolution fluorescence imaging technology with characteristics such as fast imaging speed and low phototoxicity. By projecting sinusoidal patterns at the sample plane, the high-frequency information in Fourier space which is out of the optical transfer function of the optical system is loaded into the low-frequency information and collected by the objective lens. However, due to the mechanical error of the system, the fringes in the collected data often have some deviation from the presupposed initial values. These systemic errors of fringe will directly affect the quality of the reconstructed SIM image, among which Fringe modulation depth is a very important parameter. Here, we explored the SIM reconstruction method based on the U-net neural network architecture recently reported by Luhong Jin et al.We performed a simulation to validate the method. Specifically, we use an open source fluorescent-bead images for the training and testing. We found that after training, the output of the trained neural network is very close to the ground truth, and then the super-resolution information can be well recovered from the low-modulation SIM raw images. We then further performed the similar study on the images of real biological structures which are also available as an open source dataset. Our study thus demonstrates that the deep learning neural network algorithm can significantly relax the requirement on the fringe modulation depth.Therefore, the simplified SIM system without any polarization modulation can be expected.

The exploit of magnetic devices with high magnetoresistance is vital for the development of magnetic sensing and data storage technologies. Here, using density functional calculations combined with Monte Carlo simulations, we explore the magnetic properties and spin-dependent transport of CrI3 monolayer under an electrostatic hole doping. Extraordinarily, the magnetoresistance can be controlled over 10(6)% within a certain doping density range. The hole doping can render CrI3 monolayer half-metallic and nearly 100% spin-polarization at Fermi energy level can be achieved. Moreover, the hole doping can significantly enhance the stability of itinerant ferromagnetism. The Heisenberg exchange parameters can be significantly improved and meanwhile, the Curie temperature can be boosted to room temperature via a doping density of 8.49 x 10(14) cm(-2). This study reveals that the carrier doping engineering can enable two-dimensional CrI3 as a remarkable material for developing practical and high-performance spintronic nanodevices.

This proposesa metal nanostructure composed of a metal nanodisc and a metal nanosphereon top of it, which can be applied for surface enhanced Raman scattering. Due to the excitation of the breathing mode surface plasmon resonance of the nanodisc, this nanostructure can form a gap mode with efficient longitudinal electric field enhancement under the illumination of a radially polarized vector beam. The simulation based on finite element method is carried out to investigate this gap mode and an electrical filed enhancement of 100 times relative to the valid transverse electrical field is demonstrated. In order to present more clearly the spectrum characteristic and the surface electric field distribution of this new nanostructure, the other structures including a single metal nanodisc, a single metal nanosphere, metal film and a metal nanosphere-on the metal film-are also studied under the same simulation configuration. Since the metal nanosphere can be regarded as the tip of a metal probe, the gap mode proposed here is expected to find application in tip enhanced Raman scattering.

Relying on high-sensitivity refractive index sensing and a highly constrained evanescent field of surface plasmon resonance (SPR), broadband photoacoustic (PA) pressure transients were measured using an SPR sensor instead of routinely used piezoelectric ultrasonic transducers. An acoustic cavity made from stainless steel and having a designed ellipsoidal inner surface redirected laser-induced PA waves from the PA excitation spot to the SPR sensor. By incorporating the SPR sensor with the acoustic cavity, we developed optical-resolution photoacoustic microscopy (OR-PAM) with multiple advantages, including reflection-mode signal capture, improved PA detection sensitivity, increased PA spectral bandwidth as broad as ∼98 MHz, and micrometer-scale lateral resolution. This allowed label-free volumetric PA imaging of vasculature in not only the thin ear but also the thick forelimb of living mice. With these combined advantages, our OR-PAM system potentially offers more opportunities for biomedical investigation, for example, when studying microcirculations in the eye and cortex.

The appearance of fluorescent probes has greatly promoted the development of optical microscopy, while Raman scattering detection is a further progress attracting more applications of microscopy. However, the research on the wide-field detection and imaging of Raman signals is a challenging project in recent years. We propose a method combining super-resolution structured illumination microscopy (SIM) and the wide-field narrow-bandwidth filtering technique of the tunable filter, using a digital micro mirror device (DMD) to generate structured patterns, and a pair of filters whose cutoff frequency can change as the angle of incidence changes, which allows us to obtain super-resolution images of samples at specific Raman shift peaks. We build up a microscopy system using this method, perform imaging experiments on fluorescent beads of different emission wavelengths and achieve the expected results. In the 3T3 Cell marked with SERS beads imaging experiments, good results are also achieved. We hope that this technology can be applied to more occasions, such as dynamic imaging of biological structures.

Through focusing the excitation laser, optical-resolution photoacoustic microscopy (OR-PAM) is capable of measuring optical absorption properties down to micrometer-scale lateral resolution within biological tissues. The focused Gaussian beam routinely employed in the OR-PAM setups is inadequate for acquiring the volumetric images of biological specimens with thickness from tens micrometers to millimeter without scanning in depth because of the inconsistent lateral resolution along the depth direction due to its short depth of focus (DoF). Here, we integrate a spatial light modulator (SLM) into the optical path of an OR-PAM for realizing the flexibly adjustable DoF. By simply switching the phase patterns assigned onto the SLM interface, three representative illumination beams are produced, including conventional short-DoF Gaussian beam (GB), needle-like Bessel beam (BB), and extended depth-of-focus beam (EDFB). These modulations can be well realized based on the extended Nijboer-Zernike theory. The photoacoustic excitations show variable DoFs ranging from hundreds of micrometers (GB and BB) up to 1.38 mm (EDFB) but a consistent lateral resolution of ∼3.5 μm. The proposed method is confirmed by volumetric imaging of multiple tungsten fibers positioned at different depths.

Novel urchin-like CuO/ZnO nanocomposites were synthesized using glutamine (Gln) as a growth regulator by a biomimetic hydrothermal process with subsequent calcination. The urchin-like hierarchical structure is assembled by closely packed and inter-connected nanoparticles with sizes of 20–50 nm. Gln plays key roles in phase composition and morphology due to its competitive coordination with Cu ions and Zn ions. Compared with the prepared ZnO/CuO nanosheets without Gln and pure ZnO with Gln, the urchin-like CuO/ZnO nanocomposites exhibited the most remarkable performance for the degradation of methylene blue dye under UV irradiation. On the basis of the photocatalytic mechanism of urchin-like CuO/ZnO nanocomposites, a direct relationship among the morphology, phase composition and photocatalytic properties is established.

Photoacoustic microscopy (PAM) enables the measurement of properties associated with optical absorption within tissues and complements sophisticated technologies employing optical microscopy. An inadequate frequency response as determined by a piezoelectric ultrasonic transducer results, however, in poor depth resolution and inaccurate measurements of the coefficients of optical absorption. We developed a PAM system configured as an attenuated total reflectance sensor with a ten-layer graphene film sandwiched between a prism and water (the coupling medium) for photoacoustic (PA) wave detection. Transients of the PA pressure cause perturbations in the refractive index of the water thereby changing the polarization-dependent absorption of the graphene film. The signal in PA detection involves recording the difference in the temporal-varying reflectance intensity between the two orthogonally polarized probe beams. The graphene-based sensor has an estimated noise-equivalent-pressure sensitivity of ∼550 Pa over an approximately linear pressure response from 11.0 kPa to 55.0 kPa. Moreover, it enables a much broader PA bandwidth detection of up to ∼150 MHz, primarily dominated by a highly localized evanescent field. From the strong optical absorption of inherent hemoglobin, in vivo label-free PAM imaging provided a three-dimensional viewing of the microvasculature of a mouse ear. These results suggest great potential for graphene-based PAM in biomedical investigations, such as microcirculation studies.

The spatial confinement of the tip-induced plasmon is a critical factor to determine the resolution of the tip-enhanced Raman spectroscopy (TERS) system. Despite the compressed optical field, only 1 nm is obtained by the self-interaction effect of molecule; however, the deeper physical laws underlying are still under discussion. In addition, due to the gap between the tip and the substrate is only a few nanometers (or less), the quantum effects should be taken into account. For simplicity, we treat the system of the plasmonic dimer with a molecule in the gap as a TERS-like system. In the framework of the newly developed quantum hydrodynamic model, we propose a model to study the light field enhancement and compression affected by the quantum effects in TERS-like system. The results show that the “hot spot” size depends on both the shape and the boundary location of the molecule in the dimer gap. The mechanism of such light field distribution will make us distinguish the boundary of the molecule more clearly. Therefore, our theoretical model offers a new insight into the confinement of the electromagnetic field in the TERS-like system, hence, the physical mechanism of the subnanometer spatial resolution in TERS system.

We investigate an application case study of a phase sensitive surface plasmon resonance (SPR) biosensor based on Mach–Zehnder configuration for efficient targeted drug screening, where calculating reaction kinetic constants, inhibition effect and cytotoxicity analysis are three key factors in the evaluation. As a typical targeted drug, cetuximab is selected in the measurements assisted by the phase SPR biosensor with a sensitivity of 10−6 in terms of refractive index unit (RIU) and stability of 6×10−7RIU in 80min. The reaction kinetic constants of cetuximab binding to epidermal growth factor receptor (EGFR) are found as: kd (dissociation constant)=1.75±0.29×10−3S−1, kD (equilibrium dissociation constant)=4.19±0.58nM. The results of inhibition effect analysis show that cetuximab can block EGFR binding to its two ligands, epidermal growth factor (EGF) and EGFR-transforming growth factor α (TGF-α). This effect has been tested in three cell lines of lung adenocarcinoma, colon cancer and breast cancer. Comparing to other conventional methods, we find that the phase SPR biosensor can determine the cell sensitivity to cetuximab in just 4h. As a label-free, real-time, high sensitivity and stability biosensor, the phase SPR biosensor is a potential optical technique for targeted drug screening and analysis of cell resistance to drugs with comparative advantages.