Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules

Tailoring Light-Matter Interactions in Overcoupled Resonator for Biomolecule Recognition and Detection

Plasmonic nanoantennas provide unique opportunities for precise control of light-matter coupling in surface-enhanced infrared absorption (SEIRA) spectroscopy, but most of the resonant systems realized so far suffer from the obstacles of low sensitivity, narrow bandwidth, and asymmetric Fano resonance perturbations. Here, we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient (μ) (OC-Hμ resonator) by precisely controlling the radiation loss channel, the resonator-oscillator coupling channel, and the frequency detuning channel. We observed a strong dependence of the sensing performance on the coupling state, and demonstrated that OC-Hμ resonator has excellent sensing properties of ultra-sensitive (7.25% nm-1), ultra-broadband (3-10 μm), and immune asymmetric Fano lineshapes. These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules, trace detection, and protein secondary structure analysis using a single array (array size is 100 × 100 µm2). In addition, with the assistance of machine learning, mixture classification, concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%. Finally, we demonstrated the potential of OC-Hμ resonator for SARS-CoV-2 detection. These findings will promote the wider application of SEIRA technology, while providing new ideas for other enhanced spectroscopy technologies, quantum photonics and studying light-matter interactions.

The rise of electrically tunable metasurfaces

Metasurfaces, which offer a diverse range of functionalities in a remarkably compact size, have captured the interest of both scientific and industrial sectors. However, their inherent static nature limits their adaptability for their further applications. Reconfigurable metasurfaces have emerged as a solution to this challenge, expanding the potential for diverse applications. Among the series of tunable devices, electrically controllable devices have garnered particular attention owing to their seamless integration with existing electronic equipment. This review presents recent progress reported with respect to electrically tunable devices, providing an overview of their technological development trajectory and current state of the art. In particular, we analyze the major tuning strategies and discuss the applications in spatial light modulators, tunable optical waveguides, and adaptable emissivity regulators. Furthermore, the challenges and opportunities associated with their implementation are explored, thereby highlighting their potential to bridge the gap between electronics and photonics to enable the development of groundbreaking optical systems.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Gradient High-Q Dielectric Metasurfaces for Broadband Sensing and Control of Vibrational Light-Matter Coupling

Surface-enhanced infrared absorption spectroscopy (SEIRA) has emerged as a powerful technique for ultrasensitive chemical-specific analysis. SEIRA can be realized by employing metasurfaces that can enhance light-matter interactions in the spectral bands of molecular vibrations. Increasing sample complexity emphasizes the need for metasurfaces that can operate simultaneously at different spectral bands, both accessing rich spectral information over a broad band, and resolving subtle differences in the absorption fingerprints through narrow-band resonances. Here, a novel concept of resonance-gradient metasurfaces is introduced, where the required spectral selectivity is achieved via local high-quality-factor (high-Q) resonances, while the continuous coverage of a broad band is enabled by the gradual adjustment of the unit-cell dimensions along the planar structure. The highly tailorable design of the gradient metasurfaces provides flexibility for shaping the spectral sampling density to match the relevant bands of target analytes while keeping a compact device footprint. The versatility of the gradient metasurfaces is demonstrated through several sensing scenarios, including polymer mixture deconvolution, detecting a multistep bioassay, and identification of the onset of vibrational strong coupling regime. The proposed gradient-resonance platform significantly contributes to the rapidly evolving landscape of nonlocal metasurfaces, enabling applications in molecular detection and analysis of fundamental light-matter interaction phenomena.

Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.

Advanced Terahertz Refractive Sensing And Fingerprint Recognition Through Metasurface-Excited Surface Waves

High-sensitive metasurface-based sensors are essential for effective substance detection and insightful bio-interaction studies, which compress light in subwavelength volumes to enhance light-matter interactions. However, current methods to improve sensing performance always focus on optimizing near-field response of individual meta-atom, and fingerprint recognition for bio-substances necessitates several pixelated metasurfaces to establish a quasi-continuous spectrum. Here, a novel sensing strategy is proposed to achieve Terahertz (THz) refractive sensing, and fingerprint recognition based on surface waves (SWs). Leveraging the long-range transmission, strong confinement, and interface sensitivity of SWs, a metasurface-supporting SWs excitation and propagation is experimentally verified to achieve sensing integrations. Through wide-band information collection of SWs, the proposed sensor not only facilitates refractive sensing up to 215.5°/RIU, but also enables the simultaneous resolution of multiple fingerprint information within a continuous spectrum. By covering 5 µm thickness of polyimide, quartz and silicon nitride layers, the maximum phase change of 91.1°, 101.8°, and 126.4° is experimentally obtained within THz band, respectively. Thus, this strategy broadens the research scope of metasurface-excited SWs and introduces a novel paradigm for ultrasensitive sensing functions.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Advances in terahertz metasurface graphene for biosensing and application

Based on the extraordinary electromagnetic properties of terahertz waves, such as broadband, low energy, high permeability, and biometric fingerprint spectra, terahertz sensors show great application prospects in the biochemical field. However, the sensitivity of terahertz sensing technology is increasingly required by modern sensing demands. With the development of terahertz technology and functional materials, graphene-based terahertz metasurface sensors with the advantages of high sensitivity, fingerprint identification, nondestructive and anti-interference are gradually gaining attention. In addition to providing ideas for terahertz biosensors, these devices have attracted in-depth research and development by scientists. An overview of graphene-based terahertz metasurfaces and their applications in the detection of biochemical molecules is presented. This includes sensor mechanism research, graphene metasurface index evaluation, protein and nucleic acid sensors, and other chemical molecule sensing. A comparative analysis of graphene, nanomaterials, silicon, and metals to develop material-integrated metasurfaces. Furthermore, a brief summary of the main performance results of this class of devices is presented, along with suggestions for improvements to the existing shortcoming.

Enhancement of wide-band trace terahertz absorption spectroscopy based on microstructures: a review

Research on the interaction between nanoscale materials and light holds significant scientific significance for the development of fields such as optoelectronic conversion and biosensing. The study of micro- and nano-optics has produced numerous outstanding research achievements by utilizing the dielectric optical coupling mechanism and plasmon effects to enhance the interaction between light and matter. These findings have demonstrated tremendous potential for applications in the field of molecular fingerprint sensing. This review focuses on a retrospective analysis of recent research studies in the enhancement of wide-band trace terahertz absorption spectroscopy. The physical mechanisms of using waveguide structures, dielectric metasurfaces/meta-gratings, and spoof surface plasmon polaritons (SSPs) to improve the interaction between light and trace-amount matters are introduced. The new approaches and methods for enhancing broad-band terahertz absorption spectroscopy of trace samples using microstructure designs are discussed. Additionally, we elucidate the scientific ideas and exploratory achievements in enhancing terahertz fingerprint spectroscopy detection. Finally, we provide an outlook on the research and development direction and potential practical applications of absorption spectroscopy enhancement detection.

LuMA-Functionalized Thermosensitive Hydrogel: A Versatile and Robust Dopamine-Triggered Platform for Diverse Biomolecules Sensing

It is of great significance for the analysis of multiple biomarkers because a single biomarker is difficult to accurately achieve early diagnosis, disease course monitoring, and prognosis evaluation. Herein, a luminescence thermosensitive hydrogel was synthesized by radical polymerization using a methacrylic acid derivative monomer of luminol (LuMA) as luminescent, N-isopropylacrylamide (NIPAM) as thermosensitive monomer, and acrydite-oligonucleotides [dopamine (DA) aptamer, DNA C1, and DNA C2] as recognition elements. The combined DA based on the affinity interaction between the DA and the aptamer on the hydrogel polymer chain was electrochemically oxidized to dopamine quinone during the electrochemiluminescence (ECL) scanning, which effectively quenched the ECL signal of LuMA due to the resonance energy transfer (RET). In addition, the thermosensitive hydrogel showed swelling-collapse characteristics when the temperature was below and above the volume phase transition temperature. Undergoing the collapse process initiated by the temperature, the RET efficiency was further enhanced due to the shortened distance between the energy donor and acceptor, showing a 1.4 times signal amplification and achieving sensitive detection of DA with a limit of detection (LOD) of 1.7 × 10-10 M. For a proof of concept application, coupled with the target-induced release of DA from the DNA-magnetic beads bioconjugations based on duplex-specific nuclease (DSN)-assisted target recycling amplification strategy and DNAzyme cleavage reaction, this ECL-RET approach was successfully used to evaluate multiple targets including miRNA-141 and MUC1 with the LOD of 2.5 aM and 1.6 fg/mL, respectively. The excellent performances of the versatile and robust ECL-RET hydrogel in multiple target sensing showed potential applications in clinical diagnosis and disease therapeutic assay.

Dynamic construction of refractive index-dependent vibrations using surface plasmon-phonon polaritons

One of the fundamental hurdles in infrared spectroscopy is the failure of molecular identification when their infrared vibrational fingerprints overlap. Refractive index (RI) is another intrinsic property of molecules associated with electronic polarizability, but with limited contribution to molecular identification in mixed environments currently. Here, we investigate the coupling mode of localized surface plasmon and surface phonon polaritons for vibrational de-overlapping. The coupling mode is sensitive to the molecular refractive index, attributed to the RI-induced vibrational variations of surface phonon polaritons (SPhP) within the Reststrahlen band, referred to as RI-dependent SPhP vibrations. The RI-dependent SPhP vibrations are linked to molecular RI features. According to the deep-learning-augmented demonstration of bond-breaking-bond-making dynamic profiling in biological reaction, we substantiate that the RI-dependent SPhP vibrations effectively disentangle overlapping vibrational modes, achieving a 92% identification accuracy even for the strongly overlapping vibrational modes in the reaction. Our findings offer insights into the realm of light-matter interaction and provide a valuable toolkit for biomedicine applications.

Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration

Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.

Electrochemical Wearable Biosensors and Bioelectronic Devices Based on Hydrogels: Mechanical Properties and Electrochemical Behavior

Hydrogel-based wearable electrochemical biosensors (HWEBs) are emerging biomedical devices that have recently received immense interest. The exceptional properties of HWEBs include excellent biocompatibility with hydrophilic nature, high porosity, tailorable permeability, the capability of reliable and accurate detection of disease biomarkers, suitable device-human interface, facile adjustability, and stimuli responsive to the nanofiller materials. Although the biomimetic three-dimensional hydrogels can immobilize bioreceptors, such as enzymes and aptamers, without any loss in their activities. However, most HWEBs suffer from low mechanical strength and electrical conductivity. Many studies have been performed on emerging electroactive nanofillers, including biomacromolecules, carbon-based materials, and inorganic and organic nanomaterials, to tackle these issues. Non-conductive hydrogels and even conductive hydrogels may be modified by nanofillers, as well as redox species. All these modifications have led to the design and development of efficient nanocomposites as electrochemical biosensors. In this review, both conductive-based and non-conductive-based hydrogels derived from natural and synthetic polymers are systematically reviewed. The main synthesis methods and characterization techniques are addressed. The mechanical properties and electrochemical behavior of HWEBs are discussed in detail. Finally, the prospects and potential applications of HWEBs in biosensing, healthcare monitoring, and clinical diagnostics are highlighted.

Over-coupled resonator for broadband surface enhanced infrared absorption (SEIRA)

Detection of molecules is a key issue for many applications. Surface enhanced infrared absorption (SEIRA) uses arrays of resonant nanoantennas with good quality factors which can be used to locally enhance the illumination of molecules. The technique has proved to be an effective tool to detect small amount of material. However, nanoresonators can detect molecules on a narrow bandwidth so that a set of resonators is necessary to identify a molecule fingerprint. Here, we introduce an alternative paradigm and use low quality factor resonators with large radiative losses (over-coupled resonators). The bandwidth enables to detect all absorption lines between 5 and 10 μm, reproducing the molecular absorption spectrum. Counterintuitively, despite a lower quality factor, the system sensitivity is improved and we report a reflectivity variation as large as one percent per nanometer of molecular layer of PMMA. This paves the way to specific identification of molecules. We illustrate the potential of the technique with the detection of the explosive precursor 2,4-dinitrotoluene (DNT). There is a fair agreement with electromagnetic simulations and we also introduce an analytic model of the SEIRA signal obtained in the over-coupling regime.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

Ultrasensitive Molecular Fingerprint Retrieval Using Strongly Detuned Overcoupled Plasmonic Nanoantennas

Tailoring light-matter interactions via plasmonic nanoantennas (PNAs) has emerged as a breakthrough technology for spectroscopic applications. The detuning between molecular vibrations and plasmonic resonances, as a fundamental and inevitable optical phenomenon in light-matter interactions, reduces the interaction efficiency, resulting in a weak molecule sensing signal at the strong detuning state. Here, it is demonstrated that the low interaction efficiency from detuning can be tackled by overcoupled PNAs (OC-PNAs) with a high ratio of the radiative to intrinsic loss rates, which can be used for ultrasensitive spectroscopy at strong plasmonic-molecular detuning. In OC-PNAs, the ultrasensitive molecule signals are achieved within a wavelength detuning range of 248 cm-1 , which is 173 cm-1 wider than previous works. Meanwhile, the OC-PNAs are immune to the distortion of molecular signals and maintain a lineshape consistent with the molecular signature fingerprint. This strategy allows a single device to enhance and capture the full and complex fingerprint vibrations in the mid-infrared range. In the proof-of-concept demonstration, 13 kinds of molecules with some vibration fingerprints strongly detuning by the OC-PNAs are identified with 100% accuracy with the assistance of machine-learning algorithms. This work gains new insights into detuning-state nanophotonics for potential applications including spectroscopy and sensors.

Metasurface-Enhanced Infrared Spectroscopy: An Abundance of Materials and Functionalities

Infrared spectroscopy provides unique information on the composition and dynamics of biochemical systems by resolving the characteristic absorption fingerprints of their constituent molecules. Based on this inherent chemical specificity and the capability for label-free, noninvasive, and real-time detection, infrared spectroscopy approaches have unlocked a plethora of breakthrough applications for fields ranging from environmental monitoring and defense to chemical analysis and medical diagnostics. Nanophotonics has played a crucial role for pushing the sensitivity limits of traditional far-field spectroscopy by using resonant nanostructures to focus the incident light into nanoscale hot-spots of the electromagnetic field, greatly enhancing light-matter interaction. Metasurfaces composed of regular arrangements of such resonators further increase the design space for tailoring this nanoscale light control both spectrally and spatially, which has established them as an invaluable toolkit for surface-enhanced spectroscopy. Starting from the fundamental concepts of metasurface-enhanced infrared spectroscopy, a broad palette of resonator geometries, materials, and arrangements for realizing highly sensitive metadevices is showcased, with a special focus on emerging systems such as phononic and 2D van der Waals materials, and integration with waveguides for lab-on-a-chip devices. Furthermore, advanced sensor functionalities of metasurface-based infrared spectroscopy, including multiresonance, tunability, dielectrophoresis, live cell sensing, and machine-learning-aided analysis are highlighted.

Ultrasensitive Terahertz Biodetection Enabled by Quasi-BIC-Based Metasensors

Advanced sensing devices, highly sensitive, and reliable in detecting ultralow concentrations of circulating biomarkers, are extremely desirable and hold great promise for early diagnostics and real-time progression monitoring of diseases. Nowadays, the most commonly used clinical methods for diagnosing biomarkers suffer from complicated procedures and being time consumption. Here, a chip-based portable ultra-sensitive THz metasensor is reported by exploring quasi-bound states in the continuum (quasi-BICs) and demonstrate its capability for sensing low-concentration analytes. The designed metasensor is made of the designed split-ring resonator metasurface which supports magnetic dipole quasi-BIC combining functionalized gold nanoparticles (AuNPs) conjugated with the specific antibody. Attributed to the strong near-field enhancement near the surface of the microstructure enabled by the quasi-BICs, light-analyte interactions are greatly enhanced, and thus the device's sensitivity is boosted significantly. The system sensitivity slope is up to 674 GHz/RIU, allowing for repeatable resolving detecting ultralow concentration of C-reactive protein (CRP) and Serum Amyloid A (SAA), respectively, down to 1 pM. The results touch a range that cannot be achieved by ordinary immunological assays alone, offering a novel non-destructive and rapid trace measured approach for next-generation biomedical quantitative detection systems.

Artificial intelligence-coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases

Diagnosis of neurodegenerative disorders (NDDs) including Parkinson's disease and Alzheimer's disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker-based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.

Recent Advances in Metaphotonic Biosensors

Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.

Analytical device miniaturization for the detection of circulating biomarkers

Diverse (sub)cellular materials are secreted by cells into the systemic circulation at different stages of disease progression. These circulating biomarkers include whole cells, such as circulating tumour cells, subcellular extracellular vesicles and cell-free factors such as DNA, RNA and proteins. The biophysical and biomolecular state of circulating biomarkers carry a rich repertoire of molecular information that can be captured in the form of liquid biopsies for disease detection and monitoring. In this Review, we discuss miniaturized platforms that allow the minimally invasive and rapid detection and analysis of circulating biomarkers, accounting for their differences in size, concentration and molecular composition. We examine differently scaled materials and devices that can enrich, measure and analyse specific circulating biomarkers, outlining their distinct detection challenges. Finally, we highlight emerging opportunities in biomarker and device integration and provide key future milestones for their clinical translation.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform

As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.

Binding and Functional Folding (BFF): A Physiological Framework for Studying Biomolecular Interactions and Allostery

EF-hand Ca2+-binding proteins (CBPs), such as S100 proteins (S100s) and calmodulin (CaM), are signaling proteins that undergo conformational changes upon increasing intracellular Ca2+. Upon binding Ca2+, S100 proteins and CaM interact with protein targets and induce important biological responses. The Ca2+-binding affinity of CaM and most S100s in the absence of target is weak (CaKD > 1 μM). However, upon effector protein binding, the Ca2+ affinity of these proteins increases via heterotropic allostery (CaKD < 1 μM). Because of the high number and micromolar concentrations of EF-hand CBPs in a cell, at any given time, allostery is required physiologically, allowing for (i) proper Ca2+ homeostasis and (ii) strict maintenance of Ca2+-signaling within a narrow dynamic range of free Ca2+ ion concentrations, [Ca2+]free. In this review, mechanisms of allostery are coalesced into an empirical "binding and functional folding (BFF)" physiological framework. At the molecular level, folding (F), binding and folding (BF), and BFF events include all atoms in the biomolecular complex under study. The BFF framework is introduced with two straightforward BFF types for proteins (type 1, concerted; type 2, stepwise) and considers how homologous and nonhomologous amino acid residues of CBPs and their effector protein(s) evolved to provide allosteric tightening of Ca2+ and simultaneously determine how specific and relatively promiscuous CBP-target complexes form as both are needed for proper cellular function.

Calibration-free, high-precision, and robust terahertz ultrafast metasurfaces for monitoring gastric cancers

Optical sensors, with great potential to convert invisible bioanalytical response into readable information, have been envisioned as a powerful platform for biological analysis and early diagnosis of diseases. However, the current extraction of sensing data is basically processed via a series of complicated and time-consuming calibrations between samples and reference, which inevitably introduce extra measurement errors and potentially annihilate small intrinsic responses. Here, we have proposed and experimentally demonstrated a calibration-free sensor for achieving high-precision biosensing detection, based on an optically controlled terahertz (THz) ultrafast metasurface. Photoexcitation of the silicon bridge enables the resonant frequency shifting from 1.385 to 0.825 THz and reaches the maximal phase variation up to 50° at 1.11 THz. The typical environmental measurement errors are completely eliminated in theory by normalizing the Fourier-transformed transmission spectra between ultrashort time delays of 37 ps, resulting in an extremely robust sensing device for monitoring the cancerous process of gastric cells. We believe that our calibration-free sensors with high precision and robust advantages can extend their implementation to study ultrafast biological dynamics and may inspire considerable innovations in the field of medical devices with nondestructive detection.

Towards multi-molecular surface-enhanced infrared absorption using metal plasmonics

Surface-enhanced infrared absorption (SEIRA) leads to a largely improved detection of polar molecules, compared to standard infrared absorption. The enhancement principle is based on localized surface plasmon resonances of the substrate, which match the frequency of molecular vibrations in the analyte of interest. Therefore, in practical terms, the SEIRA sensor needs to be tailored to each specific analyte. We review SEIRA sensors based on metal plasmonics for the detection of biomolecules such as DNA, proteins, and lipids. We further focus this review on chemical SEIRA sensors, with potential applications in quality control, as well as on the improvement in sensor geometry that led to the development of multiresonant SEIRA substrates as sensors for multiple analytes. Finally, we give an introduction into the integration of SEIRA sensors with surface-enhanced Raman scattering (SERS).

Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy

Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.

Infrared spectroscopy with plasmonic nanoantennas is limited by small overlap between molecules and hot spots, and sharp resonance peaks. The authors demonstrate spectral multiplexing of hook nanoantennas with gradient dimensions as ultrasensitive vibrational probes in a continuous ultra-broadband region and utilize machine learning for enhanced sensing performance.

Ultrasensitive Mid-Infrared Biosensing in Aqueous Solutions with Graphene Plasmons

Identifying nanoscale biomolecules in aqueous solutions by Fourier transform infrared spectroscopy (FTIR) provides an in situ and noninvasive method for exploring the structure, reactions, and transport of biologically active molecules. However, this remains a challenge due to the strong and broad IR absorption of water which overwhelms the respective vibrational fingerprints of the biomolecules. In this work, a tunable IR transparent microfluidic system with graphene plasmons is exploited to identify ≈2 nm-thick proteins in physiological conditions. The acquired in situ tunability makes it possible to eliminate the IR absorption of water outside the graphene plasmonic hotspots by background subtraction. Most importantly, the ultrahigh confinement of graphene plasmons (confined to ≈15 nm) permits the implementation of nanoscale sensitivity. Then, the deuterium effects on monolayer proteins are characterized within an aqueous solution. The tunable graphene-plasmon-enhanced FTIR technology provides a novel platform for studying biological processes in an aqueous solution at the nanoscale.

Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder

Infrared (IR) spectra contain detailed and extensive information about the chemical composition and bonding environment in a sample. However, this information is difficult to extract from complex heterogeneous systems because of overlapping absorptions due to different generative factors. We implement a deep learning approach to study the complex spectroscopic changes that occur in cross-linked polyethylene (PEX-a) pipe by training a β-variational autoencoder (β-VAE) on a database of PEX-a pipe spectra. We show that the β-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the β-VAE encoder to a hyperspectrum of a crack in the wall of a pipe to evaluate the spatial distribution of these learned representations. This study shows how deep learning architectures like β-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems.

Artificial Intelligence in Meta-optics

Recent years have witnessed promising artificial intelligence (AI) applications in many disciplines, including optics, engineering, medicine, economics, and education. In particular, the synergy of AI and meta-optics has greatly benefited both fields. Meta-optics are advanced flat optics with novel functions and light-manipulation abilities. The optical properties can be engineered with a unique design to meet various optical demands. This review offers comprehensive coverage of meta-optics and artificial intelligence in synergy. After providing an overview of AI and meta-optics, we categorize and discuss the recent developments integrated by these two topics, namely AI for meta-optics and meta-optics for AI. The former describes how to apply AI to the research of meta-optics for design, simulation, optical information analysis, and application. The latter reports the development of the optical Al system and computation via meta-optics. This review will also provide an in-depth discussion of the challenges of this interdisciplinary field and indicate future directions. We expect that this review will inspire researchers in these fields and benefit the next generation of intelligent optical device design.

An open source three-mirror laser scanning holographic two-photon lithography system

Two-photon polymerization is a widely adopted technique for direct fabrication of 3D and 2D structures with sub-diffraction-limit features. Here we present an open-hardware, open-software custom design for a holographic multibeam two-photon polymerization system based on a phase-only spatial light modulator and a three-mirror scanhead. The use of three reflective surfaces, two of which scanning the phase-modulated image along the same axis, allows to overcome the loss of virtual conjugation within the large galvanometric mirrors pair needed to accommodate the holographic projection. This extends the writing field of view among which the hologram can be employed for multi-beam two-photon polymerization by a factor of ~2 on one axis (i.e. from ~200μm to ~400μm), with a voxel size of ~250nm × ~1050nm (lateral × axial size), and writing speed of three simultaneous beams of 2000 voxels/s, making our system a powerful and reliable tool for advanced micro and nano-fabrications on large area.

Ethically governing artificial intelligence in the field of scientific research and innovation

Artificial Intelligence (AI) has become a double-edged sword for scientific research. While, on one hand, the incredible potential of AI and the different techniques and technologies for using it make it a product coveted by all scientific research centres and organisations and science funding agencies. On the other, the highly negative impacts that its irresponsible and self-interested use is causing, or could cause, make it a controversial tool, attracting strong criticism from those involved in the different sectors of research. This study aims to delve into the current and virtual uses of AI in scientific research and innovation in order to provide guidelines for developing and implementing a governance system to promote ethical and responsible research and innovation in the field of AI.

Multi-mode plasmonic resonance broadband LWIR metamaterial absorber based on lossy metal ring

Broadband perfect infrared wave absorption of unpolarized light over a wide range of angles in an ultrathin film is critical for applications such as thermal emitters and imaging. Although many efforts have been made in infrared broadband absorption, it is still challenging to cover the perfect absorption of broadband in the long-wave infrared band. We propose a long-wave infrared broadband, polarization, and incident angle insensitivity metamaterial absorber based on the supercell with four rings of two sizes. Broadband absorption covering the long-wave infrared band is realized by combining four PSPRs and LSPRs absorption peaks excited by the supercell structure. The absorptivity of our absorber exceeds 90% in the wavelength range of 7.76∼14µm, and the average absorptivity reaches 93.8%. The absorber maintains more than 80% absorptivity as the incident angle of unpolarized light reaches 60°, which may have promising applications for thermal emitters, infrared imaging, thermal detection.

Advances and applications of nanophotonic biosensors

Nanophotonic devices, which control light in subwavelength volumes and enhance light-matter interactions, have opened up exciting prospects for biosensing. Numerous nanophotonic biosensors have emerged to address the limitations of the current bioanalytical methods in terms of sensitivity, throughput, ease-of-use and miniaturization. In this Review, we provide an overview of the recent developments of label-free nanophotonic biosensors using evanescent-field-based sensing with plasmon resonances in metals and Mie resonances in dielectrics. We highlight the prospects of achieving an improved sensor performance and added functionalities by leveraging nanostructures and on-chip and optoelectronic integration, as well as microfluidics, biochemistry and data science toolkits. We also discuss open challenges in nanophotonic biosensing, such as reducing the overall cost and handling of complex biological samples, and provide an outlook for future opportunities to improve these technologies and thereby increase their impact in terms of improving health and safety.

Plasmonic Resonant Nanoantennas Induce Changes in the Shape and the Intensity of Infrared Spectra of Phospholipids

Surface enhanced infrared absorption spectroscopic studies (SEIRAS) as a technique to study biological molecules in extremely low concentrations is greatly evolving. In order to use the technique for identification of the structure and interactions of such biological molecules, it is necessary to identify the effects of the plasmonic electric-field enhancement on the spectral signature. In this study the spectral properties of 1,2-Dipalmitoyl-sn-glycero-3 phosphothioethanol (DPPTE) phospholipid immobilized on gold nanoantennas, specifically designed to enhance the vibrational fingerprints of lipid molecules were studied. An AFM study demonstrates an organization of the DPPTE phospholipid in bilayers on the nanoantenna structure. The spectral data were compared to SEIRAS active gold surfaces based on nanoparticles, plain gold and plain substrate (Si) for different temperatures. The shape of the infrared signals, the peak positions and their relative intensities were found to be sensitive to the type of surface and the presence of an enhancement. The strongest shifts in position and intensity were seen for the nanoantennas, and a smaller effect was seen for the DPPTE immobilized on gold nanoparticles. This information is crucial for interpretation of data obtained for biological molecules measured on such structures, for future application in nanodevices for biologically or medically relevant samples.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Artificial Intelligence of Things (AIoT) Enabled Floor Monitoring System for Smart Home Applications

To enable smart homes and relative applications, the floor monitoring system with embedded triboelectric sensors has been proven as an effective paradigm to capture the ample sensory information from our daily activities, without the camera-associated privacy concerns. Yet the inherent limitations of triboelectric sensors such as high susceptibility to humidity and long-term stability remain a great challenge to develop a reliable floor monitoring system. Here we develop a robust and smart floor monitoring system through the synergistic integration of highly reliable triboelectric coding mats and deep-learning-assisted data analytics. Two quaternary coding electrodes are configured, and their outputs are normalized with respect to a reference electrode, leading to highly stable detection that is not affected by the ambient parameters and operation manners. Besides, due to the universal electrode pattern design, all the floor mats can be screen-printed with only one mask, rendering higher facileness and cost-effectiveness. Then a distinctive coding can be implemented to each floor mat through external wiring, which permits the parallel-array connection to minimize the output terminals and system complexity. Further integrating with deep-learning-assisted data analytics, a smart floor monitoring system is realized for various smart home monitoring and interactions, including position/trajectory tracking, identity recognition, and automatic controls. Hence, the developed low-cost, large-area, reliable, and smart floor monitoring system shows a promising advancement of floor sensing technology in smart home applications.

Wafer-Scale Functional Metasurfaces for Mid-Infrared Photonics and Biosensing

Metasurfaces have emerged as a breakthrough platform for manipulating light at the nanoscale and enabling on-demand optical functionalities for next-generation biosensing, imaging, and light-generating photonic devices. However, translating this technology to practical applications requires low-cost and high-throughput fabrication methods. Due to the limited choice of materials with suitable optical properties, it is particularly challenging to produce metasurfaces for the technologically relevant mid-infrared spectral range. These constraints are overcome by realizing functional metasurfaces on almost completely transparent free-standing metal-oxide membranes. A versatile nanofabrication process is developed and implemented for highly efficient dielectric and plasmonic mid-infrared metasurfaces with wafer-scale and complementary metal-oxide-semiconductor (CMOS)-compatible manufacturing techniques. The advantages of this method are revealed by demonstrating highly uniform and functional metasurfaces, including high-Q structures enabling fine spectral selectivity, large-area metalenses with diffraction-limited focusing capabilities, and birefringent metasurfaces providing polarization control at record-high conversion efficiencies.  Aluminum plasmonic devices and their integration into microfluidics for real-time and label-free mid-infrared biosensing of proteins and lipid vesicles are further demonstrated. The versatility of this approach and its compatibility with mass-production processes bring infrared metasurfaces markedly closer to commercial applications, such as thermal imaging, spectroscopy, and biosensing.

Lanthanide upconversion and downshifting luminescence for biomolecules detection

Biomolecules play critical roles in biological activities and are closely related to various disease conditions. The reliable, selective and sensitive detection of biomolecules holds much promise for specific and rapid biosensing. In recent years, luminescent lanthanide probes have been widely used for monitoring the activity of biomolecules owing to their long luminescence lifetimes and line-like emission which allow time-resolved and ratiometric analyses. In this review article, we concentrate on recent advances in the detection of biomolecule activities based on lanthanide luminescent systems, including upconversion luminescent nanoparticles, lanthanide-metal organic frameworks, and lanthanide organic complexes. We also introduce the latest remarkable accomplishments of lanthanide probes in the design principles and sensing mechanisms, as well as the forthcoming challenges and perspectives for practical achievements.

Dichroic Circular Polarizers Based on Plasmonics for Polarization Imaging Applications

Dichroic circular polarizers (DCP) represent an important group of optical filters that transfer only that part of the incident light with the desired polarization state and absorb the remainder. However, DCPs are usually bulky and exhibit significant optical loss. Moreover, the integration of these kinds of DCP devices can be difficult and costly as different compositions of chemicals are needed to achieve the desired polarization status. Circular polarizers based on metasurfaces require only thin films in the order of hundreds of nanometers but are limited by their sensitivity to angle of incidence. Furthermore, few existing solutions offer broadband operation in the visible range. By using computational simulations, this paper proposes and analyses a plasmonic DCP structure operating in the visible, from 400 nm to 700 nm which overcomes these drawbacks. The resulting circular dichroism transmission (CDT) is more than 0.9, and the maximum transmission efficiency is greater than 78% at visible wavelengths. These CDT characteristics are largely independent of angle of incidence up to angles of 80 degrees.