Machine learning for nanoplasmonics

Emerging biomedical applications of surface-enhanced Raman spectroscopy integrated with artificial intelligence and microfluidic technologies

The integration of surface-enhanced Raman spectroscopy (SERS), artificial intelligence (AI), and microfluidics represent a transformative approach for biomedical applications. By combining the molecular sensitivity of SERS, AI-driven spectral analysis, and the precise sample handling of microfluidics, these novel integrated systems enable ultrasensitive, label-free diagnostics with minimal sample processing. The development of portable, cost-effective platforms could democratize advanced diagnostics for resource-limited settings. However, challenges such as reproducibility, clinical validation, and system integration hinder widespread adoption. This review explores these new integrated platforms, beginning with a discussion of SERS principles, their biomedical applications, and the critical roles of AI and microfluidics in enhancing analytical performance. We evaluate recent advances in the application of these integrated systems, while addressing key challenges such as substrate scalability, biocompatibility, and point-of-care translation, with a focus on nanomaterials, AI models, and lab-on-chip designs. Finally, we outline future directions, including multimodal sensing, sustainable materials, and embedded AI for real-time diagnostics, to bridge the gap between technological innovation and clinical implementation.

Isayama Y, de Freitas FAN, Santana FC, Mendes da Rocha A, Moraes TFS, Andrade LM, Versiani AF, Martins EN, Cotta EA, Rodrigues WN, Nagem RAP, da Fonseca F, Furtado CA, C. Ramirez J. Advanced Computational Techniques for Plasmonic Metasurfaces in the Detection of Neglected Infectious Diseases

This tutorial delves into the integration of plasmonic metasurfaces as cutting-edge tools for creating highly sensitive diagnostic assays tailored to neglected infectious diseases. Plasmonic metasurfaces provide a transformative approach to diagnostics by addressing common limitations of traditional methods, including slow results and high costs. This tutorial explores their application in advancing sensitive and cost-effective solutions for neglected infectious diseases. This manuscript covers the complete cycle of developing optimized, AI-driven plasmonic metasurfaces, from biofunctionalization strategies and advanced fabrication techniques to addressing scalability, regulatory challenges, and point-of-care accessibility in resource-limited settings.

DNA‑Directed Assembly of Photonic Nanomaterials for Diagnostic and Therapeutic Applications

DNA-directed assembly has emerged as a versatile and powerful approach for constructing complex structured materials. By leveraging the programmability of DNA nanotechnology, highly organized photonic systems can be developed to optimize light-matter interactions for improved diagnostics and therapeutic outcomes. These systems enable precise spatial arrangement of photonic components, minimizing material usage, and simplifying fabrication processes. DNA nanostructures, such as DNA origami, provide a robust platform for building multifunctional photonic devices with tailored optical properties. This review highlights recent progress in DNA-directed assembly of photonic nanomaterials, focusing on their applications in diagnostics and therapeutics. It provides an overview of the latest advancements in the field, discussing the principles of DNA-directed assembly, strategies for functionalizing photonic building blocks, innovations in assembly design, and the resulting optical effects that drive these developments. The review also explores how these photonic architectures contribute to diagnostic and therapeutic applications, emphasizing their potential to create efficient and effective photonic systems tailored to specific healthcare needs.

Surface chemistry-engineered perovskite quantum dot photovoltaics

The discovery and synthesis of colloidal quantum dots (QDs) was awarded the Nobel Prize in Chemistry in 2023. Recently, the development of bulk metal halide perovskite semiconductors has generated intense interest in their corresponding perovskite QDs. QDs, more broadly known as nanocrystals, constitute a new class of materials that differ from both molecular and bulk materials. They have rapidly advanced to the forefront of optoelectronic applications owing to their unique size-, composition-, surface- and process-dependent optoelectronic properties. More importantly, their ultrahigh surface-area-to-volume ratio enables various surface chemistry engineering strategies to tune and optimize their optoelectronic properties. Finally, three-dimensional confined QDs, offering nearly perfect photoluminescent quantum yield, slow hot-carrier cooling time, especially their colloidal synthesis and processing using industrially friendly solvents, have revolutionized the fields of electronics, photonics, and optoelectronics. Particularly, in emerging perovskite QD-based PVs, the advancement of surface chemistry has boosted the record power conversion efficiency (PCE) to 19.1% within a five-year period, surpassing all other colloidal QD photovoltaics (PVs). Given the rapid enhancement of device performances, perovskite QD PVs have attracted significant attention. Further study of semiconducting perovskite QDs will lead to advanced surface structures, a deeper understanding of halide perovskites, and enhanced PCE. In this review article, we comprehensively summarize and discuss the emerging perovskite QD PVs, providing insights into the impact of surface chemical design on their electronic coupling, dispersibility, stability and defect passivation. The limitations of current perovskite QDs mainly arise from their "soft" ionic nature and dynamic surface equilibrium, which lead to difficulties in the large-scale synthesis of monodispersed perovskite QDs and conductive inks for high-throughput printing techniques. We present that the development of surface chemistry is becoming a platform for further improving PCE, aiming to reach the 20% milestone. Additionally, we discuss integrating artificial intelligence to facilitate the mass-production of perovskite QDs for large-area, low-cost PV technology, which could help address significant energy challenges.

Emerging trends in SERS-based veterinary drug detection: multifunctional substrates and intelligent data approaches

Veterinary drug residues in poultry and livestock products present persistent challenges to food safety, necessitating precise and efficient detection methods. Surface-enhanced Raman scattering (SERS) has been identified as a powerful tool for veterinary drug residue analysis due to its high sensitivity and specificity. However, the development of reliable SERS substrates and the interpretation of complex spectral data remain significant obstacles. This review summarizes the development process of SERS substrates, categorizing them into metal-based, rigid, and flexible substrates, and highlighting the emerging trend of multifunctional substrates. The diverse application scenarios and detection requirements for these substrates are also discussed, with a focus on their use in veterinary drug detection. Furthermore, the integration of deep learning techniques into SERS-based detection is explored, including substrate structure design optimization, optical property prediction, spectral preprocessing, and both qualitative and quantitative spectral analyses. Finally, key limitations are briefly outlined, such as challenges in selecting reporter molecules, data imbalance, and computational demands. Future trends and directions for improving SERS-based veterinary drug detection are proposed.

Developing a real-time water quality simulation toolbox using machine learning and application programming interface

Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminated by pollutants from non-point sources, urbanization, and other sources. Consequently, real-time river water quality modeling is essential for managing and protecting rivers from contamination, and its significance is growing across various sectors, including public health, agriculture, and water treatment systems. Therefore, a real-time river water quality simulation toolbox was developed using machine learning (ML) and an application program interface (API). To create the toolbox, models that simulated water quality parameters such as chlorophyll a (Chl-a), dissolved oxygen (DO), total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) at each point in the Nakdong River were constructed. The models were constructed using Artificial neural network (ANN), Random Forest (RF), support vector machines (SVM), and data from API. Subsequently, hyperparameter optimization was conducted to enhance the model's performance. During training, the models' performances were evaluated and compared based on the data sampling method and ML algorithms. Models trained with random sampling data outperformed those trained with time-series data. Among the algorithm models that used random sampling data, the RF exhibited the best performance. The average coefficient of determination (R2) values for each water quality simulation with randomly sampled data using RF for DO, TN, TP, Chl-a, and TOC were 0.79, 0.65, 0.74, 0.45, and 0.48, respectively. For ANN, they were 0.7, 0.51, 0.64, 0.35, and 0.35, respectively, and for SVM, they were 0.73, 0.51, 0.59, 0.21, and 0.3, respectively. The Chl-a and TOC models exhibited relatively poor performance, whereas the DO, TN, and TP models demonstrated superior performance. Diversifying the input data variables is necessary to improve the performance of the Chl-a and TOC models. Sensitivity and uncertainty analyses were conducted to evaluate and enhance the models' understanding. Furthermore, using a graphic user interface (GUI) toolbox, user convenience was maximized.

AI integration into wavelength-based SPR biosensing: Advancements in spectroscopic analysis and detection

BACKGROUND

In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported. This study addresses the integration of AI methods to improve the signal-to-noise ratio (SNR) and detection accuracy of wavelength-based portable SPR biosensors.

RESULTS

We designed a deep neural network integrated with the spectral subtraction method to extract SPR responses from the proposed portable SPR biosensor. Using difference spectra as the model input, our AI model provided superior noise reduction and enhanced detection capabilities, outperforming traditional spectral feature extraction methods like dip or centroid positioning. Our study achieved a significantly amplified SNR and improved detection resolution to an impressive 10-7 RIU level. In addition, we employ Shapley Additive Explanations (SHAP) analysis to determine which parts of the input the AI model considers most important when extracting SPR response, thereby increasing the interpretability and transparency of the AI model. The results indicate that the wavelength regions considered most important by our proposed AI model are very close to the full width at half maximum (FWHM) range. This region is also recognized by traditional theory as having a significant impact on the sensitivity of SPR sensing.

SIGNIFICANCE

Integrating AI into wavelength-based portable SPR biosensing represents a significant advancement in on-site detection technologies, driving potential applications across various monitoring scenarios. Our findings highlight the AI model's effectiveness in reducing noise and enhancing detection accuracy, particularly in measurements involving low-concentration analytes. This innovation holds great promise for fields that demand real-time, high-precision, on-site detection, such as biomedical diagnostics, environmental monitoring, and biochemical analysis, setting the stage for transformative shifts in these critical areas.

Plasmonic Biosensors for Health Monitoring: Inflammation Biomarker Detection

Surface plasmon resonance (SPR) and localized SPR (LSPR) biosensors have emerged as viable technologies in the clinical detection of biomarkers for a wide array of health conditions. The success of SPR biosensors lies in their ability to monitor in real-time label-free biomarkers in complex biofluids. Recent breakthroughs in nanotechnology and surface chemistry have significantly improved this feature, notably from the incorporation of advanced nanomaterials including gold nanoparticles, graphene, and carbon nanotubes providing better SPR sensor performance in terms of detection limits, stability, and specificity. Recent progress in microfluidic integration has enabled SPR biosensors to detect multiple biomarkers simultaneously in complex biological samples. Taken together, these advances are closing the gap for their use in clinical diagnostics and point-of-care (POC) applications. While broadly applicable, the latest advancements in plasmonic biosensing are overviewed using inflammation biomarkers C-reactive protein (CRP), interleukins (ILs), tumor necrosis factor-α (TNF-α), procalcitonin (PCT), ferritin, and fibrinogen for a series of conditions, including cardiovascular diseases, autoimmune disorders, infections, and sepsis, as a key example of plasmonic biosensors for clinical applications. We highlight developments in sensor design, nanomaterial integration, surface functionalization, and multiplexing and provide a look forward to clinical applications by assessing the current limitations and exploring future directions for translating SPR biosensors for diagnostics and health monitoring. By enhancement of diagnostic accuracy, reproducibility, and accessibility, particularly in POC settings, SPR biosensors have the potential to significantly contribute to personalized healthcare and bring real-time, high-precision diagnostics to the forefront of clinical practice.

AI in SERS sensing moving from discriminative to generative

This perspective discusses the present and future role of artificial intelligence (AI) and machine learning (ML) in surface-enhanced Raman scattering (SERS) sensing. Our goal is to guide the reader through current applications, mainly focused on discriminative approaches aimed at developing new and improved SERS diagnostic capabilities, towards the future role of AI in SERS sensing, with the use of generative approaches to design new materials and biomaterials.

Machine Learning-Integrated Numerical Simulation for Predicting Photothermal Conversion Performance of Metallic Nanofluids

Photothermal conversion in metallic nanofluids, driven by localized surface plasmon resonances, is essential for applications in biomedicine, such as cancer treatment and biosensing. However, accurately predicting photothermal conversion performance, particularly the spatial temperature distribution, remains challenging due to the complex interplay of nanoparticle properties. Existing experimental methods are labor-intensive and often insufficient in providing detailed thermal profiles. Here, a novel approach that integrates machine learning is presented with numerical simulations to predict the photothermal conversion efficiency and spatial temperature distribution in gold nanorod nanofluid. The method employs Discrete Dipole Approximation for optical property calculations, Monte Carlo simulations for light transport, and finite element methods for temperature distribution modeling. The machine learning model, trained on 1,024 cases of photothermal conversion efficiency and 2,016 cases of temperature fields, achieves rapid and accurate predictions with a high correlation coefficient (R2 = 0.972) to simulation results. This approach not only streamlines the prediction process but also provides an accessible tool for optimizing nanoparticle design, with significant implications for advancing biomedicine, energy, and sensor technologies.

Nanostructured Surfaces with Plasmonic Activity and Superhydrophobicity: Review of Fabrication Strategies and Applications

Plasmonics and superhydrophobicity have garnered broad interest from academics and industry alike, spanning fundamental scientific inquiry and practical technological applications. Plasmonic activity and superhydrophobicity rely heavily on nanostructured surfaces, providing opportunities for their mutually beneficial integration. Engineering surfaces at microscopic and nanoscopic length scales is necessary to achieve superhydrophobicity and plasmonic activity. However, the dissimilar surface energies of materials commonly used in fabricating plasmonic and superhydrophobic surfaces and different length scales pose various challenges to harnessing their properties in synergy. In this review, an overview of various techniques and materials that researchers have developed over the years to overcome this challenge is provided. The underlying mechanisms of both plasmonics and superhydrophobicity are first overviewed. Next, a general classification scheme is introduced for strategies to achieve plasmonic and superhydrophobic properties. Following that, applications of multifunctional plasmonic and superhydrophobic surfaces are presented. Lastly, a future perspective is presented, highlighting shortcomings, and opportunities for new directions.

Multi-Level High Entropy-Dissipative Structure Enables Efficient Self-Decoupling of Triple Signals

The theory of high entropy-dissipative structure is confined to high-entropy alloys and their oxide materials under harsh conditions, but it is very difficult to obtain high entropy-dissipative structure for smart sensors based on polymers and metal oxides under mild conditions. Moreover, multiple signal coupling effect heavily hinder the sensor applications, and current multimodal integrated devices can solve two signal-decoupling, but need very complicated process way. In this work, new synthesis concept is the first time to fabricate high entropy-dissipative conductive layer of smart sensors with triple-signal response and self-decoupling ability within poly-pyrrole/zinc oxide (PPy/ZnO) system. The sensor (SPZ20) amplifies pressure (17.54%/kPa) and gas (0.37%/ppm), reduces humidity (0.41%/% RH) and temperature (0.12%/°C) signals, simultaneously achieving the triple self-decoupling effect of pressure and gas in the complex temperature-humidity field because of the enlarged pressure-contact area, enhanced gas-responsive sites, altered vapor path and its own heat insulation function. Additionally, it inherits the strong robustness (500 rubbing, washing, and heating or freezing cycles) and endurance (10 000 photo-purification cycles) of traditional high-entropy materials for information transmission and smart alarms in emergencies or harsh environments. This work gives a new insight into the multiple-signal response and smart flexible electronic design from natural fibers.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals.

Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid

BACKGROUND

Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes.

RESULTS

This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid.

SIGNIFICANCE

SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample, including the presence of interfering plasmids. Our approach offers a promising alternative to existing methods for monitoring antibiotic-resistant bacteria, characterized by its simplicity, low detection limit, and the potential for rapid and straightforward analysis.

Impact of Surface Enhanced Raman Spectroscopy in Catalysis

Catalysis stands as an indispensable cornerstone of modern society, underpinning the production of over 80% of manufactured goods and driving over 90% of industrial chemical processes. As the demand for more efficient and sustainable processes grows, better catalysts are needed. Understanding the working principles of catalysts is key, and over the last 50 years, surface-enhanced Raman Spectroscopy (SERS) has become essential. Discovered in 1974, SERS has evolved into a mature and powerful analytical tool, transforming the way in which we detect molecules across disciplines. In catalysis, SERS has enabled insights into dynamic surface phenomena, facilitating the monitoring of the catalyst structure, adsorbate interactions, and reaction kinetics at very high spatial and temporal resolutions. This review explores the achievements as well as the future potential of SERS in the field of catalysis and energy conversion, thereby highlighting its role in advancing these critical areas of research.

Nanoplasmonic biosensors for environmental sustainability and human health

Monitoring the health conditions of the environment and humans is essential for ensuring human well-being, promoting global health, and achieving sustainability. Innovative biosensors are crucial in accurately monitoring health conditions, uncovering the hidden connections between the environment and human well-being, and understanding how environmental factors trigger autoimmune diseases, neurodegenerative diseases, and infectious diseases. This review evaluates the use of nanoplasmonic biosensors that can monitor environmental health and human diseases according to target analytes of different sizes and scales, providing valuable insights for preventive medicine. We begin by explaining the fundamental principles and mechanisms of nanoplasmonic biosensors. We investigate the potential of nanoplasmonic techniques for detecting various biological molecules, extracellular vesicles (EVs), pathogens, and cells. We also explore the possibility of wearable nanoplasmonic biosensors to monitor the physiological network and healthy connectivity of humans, animals, plants, and organisms. This review will guide the design of next-generation nanoplasmonic biosensors to advance sustainable global healthcare for humans, the environment, and the planet.

When Metal Nanoclusters Meet Smart Synthesis

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis.

Artificial Intelligence-Enhanced Metasurfaces for Instantaneous Measurements of Dispersive Refractive Index

Measurements of the refractive index of liquids are in high demand in numerous fields such as agriculture, food and beverages, and medicine. However, conventional ellipsometric refractive index measurements are too expensive and labor-intensive for consumer devices, while Abbe refractometry is limited to the measurement at a single wavelength. Here, a new approach is proposed using machine learning to unlock the potential of colorimetric metasurfaces for the real-time measurement of the dispersive refractive index of liquids over the entire visible spectrum. The platform with a proof-of-concept experiment for measuring the concentration of glucose is further demonstrated, which holds a profound impact in non-invasive medical sensing. High-index-dielectric metasurfaces are designed and fabricated, while their experimentally measured reflectance and reflected colors, through microscopy and a standard smartphone, are used to train deep-learning models to provide measurements of the dispersive background refractive index with a resolution of ≈10-4, which is comparable to the known index as measured with ellipsometry. These results show the potential of enabling the unique optical properties of metasurfaces with machine learning to create a platform for the quick, simple, and high-resolution measurement of the dispersive refractive index of liquids, without the need for highly specialized experts and optical procedures.

Gated Recurrent Neural Network for Predicting the Plasmonic Colloid Composition from Spectra

In current research on the synthesis of colloidal nanostructures, the size and morphology of nanoparticles still exhibit certain dispersion and variation from batch to batch. Characterization of size distribution and morphology distribution of nanoparticles often requires techniques such as scanning electron microscopy or transmission electron microscopy, which involve high vacuum environments, are time-consuming, and costly. Experienced researchers can roughly estimate the size and distribution of nanostructure from spectra for a given synthetic route, but the accuracy is often limited. This paper reports the potential of using neural networks to accurately predict the composition of colloidal nanostructures from spectra. We address several fundamental issues in neural network prediction of colloidal composition. We first demonstrate the prediction of the composition of a colloidal binary mixture of gold nanoparticles using a gated recurrent neural network (GRU). The evolution of prediction errors for scattering, absorption, and extinction spectra of nanostructures with sizes ranging from 5 to 120 nm are analyzed. Furthermore, we demonstrate that the neural network model operates robustly under white noise in experimental testing scenarios. Compared to fully connected neural networks, the gated recurrent unit exhibits better testing accuracy in spectral prediction. When confronted with experimental data that deviates from simulation outputs, minor adjustments to the training set can allow the predictions to align closely with the experimental spectra, paving the way for the characterization of complex colloidal compositions with artificial intelligence.

Tetrodotoxin and the state-of-the-art progress of its associated analytical methods

Tetrodotoxin (TTX), which is found in various marine organisms, including pufferfish, shellfish, shrimp, crab, marine gastropods, and gobies, is an effective marine toxin and the cause of many seafood poisoning incidents. Owing to its toxicity and threat to public health, the development of simple, rapid, and efficient analytical methods to detect TTX in various food matrices has garnered increasing interest worldwide. Herein, we reviewed the structure and properties, origin and sources, toxicity and poisoning, and relevant legislative measures of TTX. Additionally, we have mainly reviewed the state-of-the-art progress of analytical methods for TTX detection in the past five years, such as bioassays, immunoassays, instrumental analysis, and biosensors, and summarized their advantages and limitations. Furthermore, this review provides an in-depth discussion of the most advanced biosensors, including cell-based biosensors, immunosensors, and aptasensors. Overall, this study provides useful insights into the future development and wide application of biosensors for TTX detection.

Harnessing the Missing Spectral Correlation for Metasurface Inverse Design

A long-held tenet in computer science asserts that the training of deep learning is analogous to an alchemical furnace, and its "black box" signature brings forth inexplicability. For electromagnetic metasurfaces, the related intelligent applications also get stuck into such a dilemma. Although the past 5 years have witnessed a proliferation of deep learning-based works across complex photonic scenarios, they neglect the already existing but untapped physical laws. Here, the intrinsic correlation between the real and imaginary parts of the spectra are revealed using Kramers-Kronig relations, which is then mimicked by bidirectional information flow in neural network space. Such consideration harnesses the missing spectral connection to extract crucial features effectively. The bidirectional recurrent neural network is benchmarked in metasurface inverse design and compare it with a fully-connected neural network, unidirectional recurrent neural network, and attention-based transformer. Beyond the improved accuracy, the study examines the intermediate information products and physically explains why different network structures yield different performances. The work offers explicable perspectives to utilize physical information in the deep learning field and facilitates many data-intensive research endeavors.

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review

The careful arrangement of nanomaterials (NMs) holds promise for revolutionizing various fields, from electronics and biosensing to medicine and optics. This review delves into the intricacies of nano-assembly (NA) techniques, focusing on oriented-assembly methodologies and stimuli-dependent approaches. The introduction provides a comprehensive overview of the significance and potential applications of NA, setting the stage for review. The oriented-assembly section elucidates methodologies for the precise alignment and organization of NMs, crucial for achieving desired functionalities. The subsequent section delves into stimuli-dependent techniques, categorizing them into chemical and physical stimuli-based approaches. Chemical stimuli-based self-assembly methods, including solvent, acid-base, biomolecule, metal ion, and gas-induced assembly, are discussed in detail by presenting examples. Additionally, physical stimuli such as light, magnetic fields, electric fields, and temperature are examined for their role in driving self-assembly processes. Looking ahead, the review outlines futuristic scopes and perspectives in NA, highlighting emerging trends and potential breakthroughs. Finally, concluding remarks summarize key findings and underscore the significance of NA in shaping future technologies. This comprehensive review serves as a valuable resource for researchers and practitioners, offering insights into the diverse methodologies and potential applications of NA in interdisciplinary research fields.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

High-Throughput All-Optical Determination of Nanorod Size and Orientation

As a single-particle characterization technique, optical microscopy has transformed our understanding of structure-function relationships of plasmonic nanoparticles, but the need for ex-situ-correlated electron microscopy to obtain structural information handicaps an otherwise exceptional high-throughput technique. Here, we present an all-optical alternative to electron microscopy to accurately and quickly extract structural information about single gold nanorods (Au NRs) using calcite-assisted localization and kinetics (CLocK) microscopy. Color CLocK images of single Au NRs allow scattering from the longitudinal and transverse plasmon modes to be imaged simultaneously, encoding spectral data in CLocK images that can then be extracted to obtain Au NR size and orientation. Moreover, through the use of convolutional neural networks, Au NR length, width, and aspect ratio can be predicted directly from color CLocK images within ∼10% of the true value measured by electron microscopy.

Nanosensor based approaches for quantitative detection of heparin

Heparin, being a widely employed anticoagulant in numerus clinical complications, requires strict quantification and qualitative screening to ensure the safety of patients from potential threat of thrombocytopenia. However, the intricacy of heparin's chemical structures and low abundance hinders the precise monitoring of its level and quality in clinical settings. Conventional laboratory assays have limitations in sensitivity and specificity, necessitating the development of innovative approaches. In this context, nanosensors emerged as a promising solution due to enhanced sensitivity, selectivity, and ability to detect heparin even at low concentrations. This review delves into a range of sensing approaches including colorimetric, fluorometric, surface-enhanced Raman spectroscopy, and electrochemical techniques using different types of nanomaterials, thus providing insights of its principles, capabilities, and limitations. Moreover, integration of smart-phone with nanosensors for point of care diagnostics has also been explored. Additionally, recent advances in nanopore technologies, artificial intelligence (AI) and machine learning (ML) have been discussed offering specificity against contaminants present in heparin to ensure its quality. By consolidating current knowledge and highlighting the potential of nanosensors, this review aims to contribute to the advancement of efficient, reliable, and economical heparin detection methods providing improved patient care.

Persistent Luminescence Lifetime-Based Near-Infrared Nanoplatform via Deep Learning for High-Fidelity Biosensing of Hypochlorite

In light of deep tissue penetration and ultralow background, near-infrared (NIR) persistent luminescence (PersL) bioprobes have become powerful tools for bioapplications. However, the inhomogeneous signal attenuation may significantly limit its application for precise biosensing owing to tissue absorption and scattering. In this work, a PersL lifetime-based nanoplatform via deep learning was proposed for high-fidelity bioimaging and biosensing in vivo. The persistent luminescence imaging network (PLI-Net), which consisted of a 3D-deep convolutional neural network (3D-CNN) and the PersL imaging system, was logically constructed to accurately extract the lifetime feature from the profile of PersL intensity-based decay images. Significantly, the NIR PersL nanomaterials represented by Zn1+xGa2-2xSnxO4: 0.4 % Cr (ZGSO) were precisely adjusted over their lifetime, enabling the PersL lifetime-based imaging with high-contrast signals. Inspired by the adjustable and reliable PersL lifetime imaging of ZGSO NPs, a proof-of-concept PersL nanoplatform was further developed and showed exceptional analytical performance for hypochlorite detection via a luminescence resonance energy transfer process. Remarkably, on the merits of the dependable and anti-interference PersL lifetimes, this PersL lifetime-based nanoprobe provided highly sensitive and accurate imaging of both endogenous and exogenous hypochlorite. This breakthrough opened up a new way for the development of high-fidelity biosensing in complex matrix systems.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning

For two-dimensional (2D) materials, the exact thickness of the material often dictates its physical and chemical properties. The 2D quantum material WTe2 possesses properties that vary significantly from a single layer to multiple layers, yet it has a complicated crystal structure that makes it difficult to differentiate thicknesses in atomic-resolution images. Furthermore, its air sensitivity and susceptibility to electron beam-induced damage heighten the need for direct ways to determine the thickness and atomic structure without acquiring multiple measurements or transferring samples in ambient atmosphere. Here, we demonstrate a new method to identify the thickness up to ten van der Waals layers in Td-WTe2 using atomic-resolution high-angle annular dark-field scanning transmission electron microscopy image simulation. Our approach is based on analyzing the intensity line profiles of overlapping atomic columns and building a standard neural network model from the line profile features. We observe that it is possible to clearly distinguish between even and odd thicknesses (up to seven layers), without using machine learning, by comparing the deconvoluted peak intensity ratios or the area ratios. The standard neural network model trained on the line profile features allows thicknesses to be distinguished up to ten layers and exhibits an accuracy of up to 94% in the presence of Gaussian and Poisson noise. This method efficiently quantifies thicknesses in Td-WTe2, can be extended to related 2D materials, and provides a pathway to characterize precise atomic structures, including local thickness variations and atomic defects, for few-layer 2D materials with overlapping atomic column positions.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.

Machine Learning-Assisted Automatically Electrochemical Addressable Cytosensing Arrays for Anticancer Drug Screening

The high-throughput and accurate screening of anticancer drugs is crucial to the preclinical assessment of candidate drugs and remains challenging. Herein, an automatically electrochemical addressable cytosensor (AEAC) for the efficient screening of anticancer drugs is reported. This sensor consists of sectionalized laser-induced graphene arrays decorated by the rhombohedral TiO2 and spherical Pt nanoparticles (LIG-TiO2-Pt) with high electrocatalytic activity for H2O2 and a homemade Ag/Pt electrode couple fixed onto the robot arm. The immobilization of laminin on the surface of LIG-TiO2-Pt can promote its biocompatibility for the growth and proliferation of various tumor cells, which empowers the in situ monitoring of H2O2 directly released from these live cells for drug screening. A machine learning (ML) algorithm is employed to eliminate the possible random or systematic errors of AEAC, realizing rapid, high-throughput, and accurate prediction of different types of anticancer drugs. This ML-assisted AEAC provides a powerful approach to accelerate the evolution of sensing-served tumor therapy.

Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.

Bimetallic copper palladium nanorods: plasmonic properties and palladium content effects

Cu is an inexpensive alternative plasmonic metal with optical behaviour comparable to Au but with much poorer environmental stability. Alloying with a more stable metal can improve stability and add functionality, with potential effects on the plasmonic properties. Here we investigate the plasmonic behaviour of Cu nanorods and Cu-CuPd nanorods containing up to 46 mass percent Pd. Monochromated scanning transmission electron microscopy electron energy-loss spectroscopy first reveals the strong length dependence of multiple plasmonic modes in Cu nanorods, where the plasmon peaks redshift and narrow with increasing length. Next, we observe an increased damping (and increased linewidth) with increasing Pd content, accompanied by minimal frequency shift. These results are corroborated by and expanded upon with numerical simulations using the electron-driven discrete dipole approximation. This study indicates that adding Pd to nanostructures of Cu is a promising method to expand the scope of their plasmonic applications.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.