All-optical controlled-NOT logic gate achieving directional asymmetric transmission based on metasurface doublet

Optical logic gates play important roles in all-optical logic circuits,which lie at the heart of the next-generation optical computing technology.However,the intrinsic contradiction between compactness and robustness hinders the development in this field.Here,we propose a simple design principle that can possess multiple-input-output states according to the incident circular polarization and direction based on the metasurface doublet,which enables controlled-NOT logic gates in infrared region.Therefore,the directional asymmetric electromagnetic transmission can be achieved.As a proof of concept,a spin-dependent Janus metasurface is designed and experimentally verified that four distinct images corresponding to four input states can be captured in the far-field.In addition,since the design method is derived from geometric optics,it can be easily applied to other spectra.We believe that the proposed metasurface doublet may empower many potential applications in chiral imaging,chiroptical spectroscopy and optical computing.

Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

The size and shape of the foveal avascular zone(FAZ)have a strong positive correlation with several vision-threatening ret inovascular diseases.The identification,segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment.We presented an adaptive watershed algorithm to automatically extract F AZ from retinal optical coherence tomography angiography(OCTA)images.For the traditional watershed algorithm,"over-segmentation"is the most common problem.FAZ is often incorrectly divided into multiple regions by redundant"dams".This paper analyzed the relationship between the"dams"length and the maximum inscribed circle radius of FAZ,and proposed an adaptive watershed algorithm to solve the problem of"over-segmentation".Here,132 healthy retinal images and 50 diabetic retinopathy(DR)images were used to verify the accuracy and stability of the algorithm.Three ophthal-mologists were invited to make quan titative and qualitative evaluations on the segmentation results of this algorithm.The quantitative evaluation results show that the correlation coffi-cients between the automatic and manual segmentation results are 0.945(in healthy subjects)and 0.927(in DR patients),respectively.For qualitative evaluation,the percentages of"perfect segmentation"(score of 3)and"good segmentation"(score of 2)are 99.4%(in healthy subjects)and 98.7%(in DR patients),respectively.This work promotes the application of watershed algorithm in FAZ segmentation,making it a useful tool for analyzing and diagnosing eye diseases.

Research on Fiber Optic Surface Plasmon R esonance.Biosensors:A Review

Due to the benefits of the high sensitivity,real-time response,no labeling requirement,and good selectivity,fiber optic sensors based on surface plasmon resonance(SPR)have gained popularity in biochemical sensing in recent years.The current research on such sensors is hot in enhancing sensitivity,improving detection accuracy,and achieving the detection of biochemical molecules.The goal of this work is to present a thorough overview of recent developments in the optical fiber SPR biosensor research.Firstly,it explores the basic principles and sensing structures of optical fiber SPR biosensors,focusing on four aspects.Subsequently,this paper introduces three fiber optic surface plasmon biosensors:SPR,localized surface plasmon resonance(LSPR),and long-range surface plasmon resonance(LRSPR).Each concept is explained from the perspective of the basic principles of fiber optic SPR biosensors.Furthermore,a classification of fiber optic SPR biosensors in health monitoring,food safety,environmental monitoring,marine detection,and other applications is introduced and analyzed.Eventually,this paper summarizes the current research directions of SPR biosensors.Meanwhile,it provides a prospective outlook on how fiber optic SPR sensors will develop in the future.

Random Raman Fiber Laser as a Liquid Refractive Index Sensor

In this paper,a new concept of forward-pumped random Raman fiber laser(RRFL)-based liquid refractive index sensing is proposed for the first time.For liquid refractive index sensing,the flat fiber end immersed in the liquid can act as the point reflector for generating random fiber lasing and also as the sensing head.Due to the high sensitivity of the output power of the RRFL to the reflectivity provided by the point reflector in the ultralow reflectivity regime,the proposed RRFL is capable of achieving liquid refractive index sensing by measuring the random lasing output power.We theoretically investigate the effects of the operating pump power and fiber length on the refractive index sensitivity for the proposed RRFL.As a proof-of-concept demonstration,we experimentally realize high-sensitivity half-open short-cavity RRFL-based liquid refractive index sensing with the maximum sensitivity and the sensing resolution of-39.88W/RIU and 2.5075×10^(-5) RIU,respectively.We also experimentally verify that the refractive index sensitivity can be enhanced with the shorter fiber length of the RRFL.This work extends the application of the random fiber laser as a new platform for highly-sensitive refractive index sensing in chemical,biomedical,and environmental monitoring applications,etc.

Hydrogel and Machine Learning for Soft Robots’ Sensing and Signal Processing: A Review

The soft robotics field is on the rise. The highly adaptive robots provide the opportunity to bridge the gap between machines and people. However, their elastomeric nature poses significant challenges to the perception, control, and signal processing. Hydrogels and machine learning provide promising solutions to the problems above. This review aims to summarize this recent trend by first assessing the current hydrogel-based sensing and actuation methods applied to soft robots. We outlined the mechanisms of perception in response to various external stimuli. Next, recent achievements of machine learning for soft robots’ sensing data processing and optimization are evaluated. Here we list the strategies for implementing machine learning models from the perspective of applications. Last, we discuss the challenges and future opportunities in perception data processing and soft robots’ high level tasks.

Electrochemical response of MoS_(2) nanosheets/SiO_(2)-NH_(2) nanoparticles modified glassy carbon electrode to phenylacetic acid

Phenylacetic acid(PAA)is a primary raw material for illegal Methamphetamine(MATM)synthesis under the strong precursor chemicals supervisions of safrole and isosafrole.Therefore,trace detection of PAA at ultra-low concentration is a strategic technique and an urgent issue in the field of drug control.In this paper,trace determination of PAA at sub-nmol-L-1 concentration level is achieved by hydrogen bond adsorption and electrochemical catalysis through the prepared aminated SiO_(2)nanoparticles(SiO_(2)-NH_(2) NPs)and MoS_(2) nanosheets(NSs)modified glassy carbon electrode(GCE).The prepared MoS_(2) NS s/SiO_(2)-NH_(2) NPs modified electrode represents a detecting limit of 0.0989 nmol·L^(-1)and an obvious increasing linear range before the concentration increasement up to 60 nmol·L^(-1)in square wave voltammetry(SWV)responses of PAA.The SWV response of the modified electrode to PAA in the concentration range within 100 nmol·L^(-1)is higher than phenol,acetic acid(HOAc)and benzoic Acid(BEN).This electrochemical method for trace detection of PAA in aqueous solution with desired performance provides a feasible scheme for the detection of other drugs and aromatic precursor chemicals.

Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible

Replacing electrons with photons is a compelling route toward high-speed,massively parallel,and low-power artificial intelligence computing.Recently,diffractive networks composed of phase surfaces were trained to perform machine learning tasks through linear optical transformations.However,the existing architectures often comprise bulky components and,most critically,they cannot mimic the human brain for multitasking.Here,we demonstrate a multi-skilled diffractive neural network based on a metasurface device,which can perform on-chip multi-channel sensing and multitasking in the visible.The polarization multiplexing scheme of the subwavelength nanostructures is applied to construct a multi-channel classifier framework for simultaneous recognition of digital and fashionable items.The areal density of the artificial neurons can reach up to 6.25×10^(6)mm^(-2) multiplied by the number of channels.The metasurface is integrated with the mature complementary metal-oxide semiconductor imaging sensor,providing a chip-scale architecture to process information directly at physical layers for energy-efficient and ultra-fast image processing in machine vision,autonomous driving,and precision medicine.

Atomically thin transition metal dichalcogenides for the hydrogen evolution reaction

Hydrogen is a sustainable and environmentally friendly fuel produced by electrolytic water splitting.This re-quires efficient and easily accessible electrocatalysts to minimize energy consumption.Recently,as a substitute to the conventional noble-metal-based catalysts,two-dimensional transition metal dichalcogenides(TMDs)have demonstrated their potential as inexpensive catalysts for the hydrogen evolution reaction(HER).In this review,we offer an overview of recent progresses in the development of layered TMDs for the HER.The improvements in TMD catalytic activity in terms of nanostructuring,defect and boundary engineering,heteroatom doping,and interaction with the supporting material are presented.We summarize with a perspective on the challenges for the future development of highly effective TMD catalysts for the HER.

Extremely narrow resonant linewidths in metal-dielectric heterostructures

Plasmonic high-quality factor resonators with narrow surface plasmon resonance(SPR)linewidths are extremely significant for surface-enhanced Raman scattering,optical sensors,imaging,and color filters.Unfortunately,extensive research on narrowing SPR linewidths is mainly based on noble metal nanostructures that are restricted by intrinsic loss.Here,heterostructures consisting of metal and dielectric metaphotonics are experimentally designed and fabricated for elaborating SPR linewidths.The results demonstrate that the SPR linewidths can be narrowed by 66.7%relative to that of aluminum nanostructures.The resonant linewidths are directly shrunk due to the interaction between low loss in the semiconductor nanostructures and electromagnetic confinement in the metal counterparts.Meanwhile,the resonant wavelength governed by heterostructure configurations shifts from600 to 930 nm.This work will pave an avenue toward controlling resonant linewidths of metaldielectric heterostructures for numerous applications.

Highly sensitive salinity sensor based on Mach-Zehnder interferometer with double-C fiber

This paper proposes a highly sensitive,compact,and low-cost optical fiber salinity sensor based on the Mach-Zehnder interferometer.The sensor is constructed using a single mode fiber(SMF)-no-core fiber-double-C fiber(DCF)-NCF-SMF structure,with the DCF prepared by etching the dual side-hole fiber with HF acid.The DCF’s large-size exposed microfluidic channels solve the previous microstructured optical fiber’s challenging liquid filling and replacement problems.Theoretical simulations and experiments demonstrate that the sensor is suitable for high-sensitivity salinity measurement.The sensor exhibits a high salinity sensitivity of-2.26 nm/‰ in the salinity range of 10‰-50‰,as demonstrated by the experimental results.Additionally,the sensor exhibits some fascinating characteristics,including high repeatability,hysteresis,reversibility,and stability.