Deep Learning Enabled SERS Identification of Gaseous Molecules on Flexible Plasmonic MOF Nanowire Films

PCA-assisted direct diagnosis of Aβ proteins for Alzheimer's disease using non-metallic SERS platform of graphitic carbon nitride@metal-organic framework

Here, we explored a non-metallic surface enhanced Raman scattering (SERS) platform based on graphitic carbon nitride@metal-organic framework (g-C3N4@MOF) for the sensitive direct diagnosis of Aβ proteins, assisted by principal component analysis (PCA). By systematically optimizing the deposition voltage and time, we successfully achieved a uniform coating of g-C3N4 nanosheets over a large-area copper foil during the initial electrodeposition step. Subsequently, a homogeneous layer of flower-like MOF structures was deposited onto the g-C3N4 nanosheets through a secondary electrodeposition process. This cooperation of g-C3N4 nanosheets and flower-like MOF not only significantly enlarged the effective area for molecular enrichment but also promoted charge transfer through energy-level matching between the two materials. The characteristic SERS spectra of Aβ40 and Aβ42, enhanced by the g-C3N4@MOF composite substrate, were recorded and classified using PCA to extract informative features of these important biomarkers. This research exploits a new avenue for the clinical assay of neurodegenerative diseases by extracting informative features of key biomarkers.

Breakthrough in plasmonic enhanced MOFs: Design, synthesis, and catalytic mechanisms for various photocatalytic applications

Integrating metal-organic framework MOFs with plasmonic nanoparticles (NPs) addresses a significant shortcoming of standard plasmonic platforms: their low efficacy with non-adsorbing compounds. The corporation of porous MOFs complements the plasmonic characteristics, allowing for a broader range of applications. This study highlights recent advancements in the design, synthesis, structural engineering, and functional properties of heterostructures combining plasmonic NPs with MOFs, focusing on their plasmonic and catalytic reaction behaviors. These developments have greatly enhanced the protentional of plasmonic NPs-MOFs heterojunction in nanofabrication and various applications, such as chemical sensing techniques like localized surface plasmon resonance (LSPR) surface-enhanced Raman scattering (SERS) and surface-enhanced infrared absorbance (SEIRA). Additionally, the study thoroughly examines the interface interaction and photocatalytic performance of plasmonic NPs-MOFs. Various practical applications of plasmonic NPs-MOFs heterojunction are explored, including their promising role in tackling environmental challenges like industrial water pollution. Furthermore, we have a detailed discussion of various photocatalysis processes, including water splitting, CO2 reduction, pollutant degradation, and various sensing applications. Identifying current limitations and outlining future research directions to bridge existing knowledge gaps, including interface interaction, photocatalytic performance, and practical applications providing a comprehensive understanding, are the main aims of this review to inspire the development of next-generation plasmonic NPs-MOFs materials. It concludes by discussing future directions and challenges in composite development, emphasizing their potential to provide sustainable and efficient solutions for environmental remediation and energy conversion.

Research progress on regulation strategies for surface-enhanced Raman spectroscopy

As a highly sensitive analytical technology, surface enhanced Raman spectroscopy (SERS) based on localized surface plasmon resonance has been widely explored in the field of environment monitoring, food safety, material identification and biomedicine. In the field of biosensing, the design of sensing models, the regulation of enhancement factors (EFs), and the stability of detection results have always been crucial research keys. Progress in these areas has continuously expanded the application scope of SERS technology and improved the feasibility of its application. Among them, the regulation of EFs through physical enhancement and chemical enhancement is a crucial point in improving the performance of SERS. Starting from the physicochemical mechanism, this review discusses the relevant influencing parameters and then summarizes the latest regulation strategies based on the above theory, as well as special regulation methods such as E-SERS. A diverse array of regulation strategies underpinned by the SERS enhancement mechanism have been effectively harnessed to amplify the EF of the SERS system. These include a wide spectrum of metal nanostructures based on the electromagnetic mechanism (EM), as well as regulation approaches predicated on the chemical mechanism (CM), such as energy-level manipulation, defect engineering, and material coupling. In addition, it encompasses specialized regulation methods such as analyte pre-concentration. This article focuses on summarizing the principal regulation approaches that have significantly impacted SERS enhancement in recent years, complemented by specialized regulation methods, with the hope of facilitating smoother progress in future work related to SERS.

Harnessing Surface-Enhanced Raman Spectroscopy for Breath-Based Diagnostics

Human breath contains a myriad of analytes that are associated with human health conditions, making it a noninvasive source of biomarkers. Researchers and engineers are pushing the boundaries of breath analysis for the next generation of diagnostics. Breath analysis with surface enhanced Raman spectroscopy, which allows for the identification of molecular fingerprints in a simple way, represents a powerful platform for the next generation of sensors which are expected to provide tools for personalized healthcare and preventive medicine.

Deep Learning-Assisted Sensor Array Based on Host-Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives

Accurate and rapid discrimination of multiple explosives with high precision is of paramount importance for national security, ecological protection, and human health yet remains a significant challenge with conventional analytical techniques. Herein, we present an innovative deep learning-assisted artificial vision platform based on cyclodextrin-protected multicolor fluorescent gold nanoclusters (CD-AuNCs) with four distinct emission wavelengths, enabling the highly accurate discrimination of seven explosives. The sensor array leverages the host-guest interactions between the cyclodextrin ligands on the AuNCs' surface and the target explosives, generating unique fluorescence fingerprint patterns. Mechanistic studies reveal that the fluorescence enhancement of CD-AuNCs is attributed to ligand rigidification, while fluorescence quenching is primarily caused by photoinduced electron transfer between CD-AuNCs and explosives. The multicolor fluorescence responses are captured by using a smartphone, and the corresponding RGB values are simultaneously extracted. To enhance the recognition accuracy, a dense convolutional network (DenseNet) algorithm with advanced image recognition capability is integrated with the fluorescence sensor array. This platform achieves remarkable 100% recognition accuracy at a concentration of 200 μM, enabling the rapid and precise visual classification of explosives. The proposed strategy not only provides a powerful tool for on-site explosive monitoring but also offers a versatile platform for the intelligent detection of diverse analytes, demonstrating significant potential for real-world applications in environmental and security monitoring.

pH-Responsive DNA-Functionalized Liquid Metal-Organic Frameworks (L-MOFs) as Molecular Sponges for Ultrasensitive and Label-Free SERS Detection of Folic Acid

Although "hotspots" have been utilized to enhance Raman signals for detecting various biomolecules, precisely regulating "hotspot" dimensions within enhancement substrates remains a significant challenge. This study introduces a novel, easily fabricated surface-enhanced Raman spectroscopy sensor, T6(OH⁻)/Ag@CC. This platform employs single-stranded DNA of adjustable lengths to mediate the self-assembly of silver nanoparticles (Ag NPs), resulting in a uniformly enhanced substrate with a spatially organized metal-organic frameworks architecture. The DNA-mediated self-assembly exhibits pH-responsive characteristics, enabling precise control over "hotspot" distribution. Comprehensive characterization and Raman enhancement experiments demonstrate that optimal self-assembly and signal amplification are achieved under alkaline conditions. The sensor demonstrates excellent reproducibility and sensitivity, enabling the label-free detection of folic acid with a detection limit as low as 0.1 ng mL-1. Validation using real-world food and biological samples highlights its ability to accurately detect and identify folic acid fingerprints in spinach, chicken liver, and various human biological fluids, including breast milk, serum, erythrocytes, and urine. The analysis of characteristic peak intensities underscores the potential of this method as a versatile and unified approach for folic acid detection across diverse sample matrices.

A Review on the Recent Progress of Metal-Organic Frameworks Based Surface Enhanced Raman Scattering Sensors

Surface enhanced Raman scattering (SERS) has evolved into a significant fingerprint spectroscopic technique for rapidly and nonintrusively tracing target analytes through effective SERS substrates. Metal-organic frameworks (MOFs), as a boom crystalline porous material, serve as promising SERS substrates by accommodating noble metal nanoparticles (NPs) to produce MOFs-based SERS-active materials. Recently, MOFs-based SERS materials (MNPs/MOFs) have gained significant attention due to their enhanced sensing performance. The unique porous nature of MOFs provides an efficient capture capability for analytes, while their shells prevent NPs from oxidization and corrosion, thereby enhancing the consistency of SERS substrates. So far, numerous MNPs/MOFs sensors have been documented. This review outlines the research progress of MNPs/MOFs composites, focusing on the classification, synthesis strategies, and applications in environment analysis, real-time monitoring, food safety, etc.

Surface-enhanced spectroscopy technology based on metamaterials

Surface-enhanced spectroscopy technology based on metamaterials has flourished in recent years, and the use of artificially designed subwavelength structures can effectively regulate light waves and electromagnetic fields, making it a valuable platform for sensing applications. With the continuous improvement of theory, several effective universal modes of metamaterials have gradually formed, including localized surface plasmon resonance (LSPR), Mie resonance, bound states in the continuum (BIC), and Fano resonance. This review begins by summarizing these core resonance mechanisms, followed by a comprehensive overview of six main surface-enhanced spectroscopy techniques across the electromagnetic spectrum: surface-enhanced fluorescence (SEF), surface-enhanced Raman scattering (SERS), surface-enhanced infrared absorption (SEIRA), terahertz (THz) sensing, refractive index (RI) sensing, and chiral sensing. These techniques cover a wide spectral range and address various optical characteristics, enabling the detection of molecular fingerprints, structural chirality, and refractive index changes. Additionally, this review summarized the combined use of different enhanced spectra, the integration with other advanced technologies, and the status of miniaturized metamaterial systems. Finally, we assess current challenges and future directions. Looking to the future, we anticipate that metamaterial-based surface-enhanced spectroscopy will play a transformative role in real-time, on-site detection across scientific, environmental, and biomedical fields.

Breath Analysis via Surface Enhanced Raman Spectroscopy

Breath analysis is increasingly recognized as a powerful noninvasive diagnostic technique, and a plethora of exhaled volatile biomarkers have been associated with various diseases. However, traditional analytical methodologies are not amenable to high-throughput diagnostic applications at the point of need. An optical spectroscopic technique, surface-enhanced Raman spectroscopy (SERS), mostly used in the research setting for liquid sample analysis, has recently been applied to breath-based diagnostics. This promising noninvasive diagnostic tool has been demonstrated for the identification of various diseases, including lung cancer, gastric cancer, and diabetes. The versatility of SERS has enabled the use of different diagnostic strategies and allowed for fast and accurate detection of small analytes in exhaled breath. In this review, we provide an overview of recent advances in SERS-based breath analysis, focusing on sensors for the detection of gases and volatile organic compounds (VOCs) in exhaled breath, and highlight generic strategies for sample preconcentration and methods for spectral analysis. We aim to provide an overview of the state of the art and inspiration for further SERS investigation of expiration.

Nano-Metal-Organic Frameworks and Nano-Covalent-Organic Frameworks: Controllable Synthesis and Applications

Nanoscale framework materials have attracted extensive attention due to their diverse morphology and good properties, and synthesis methods of different size structures have been reported. Therefore, the relationship between different sizes and performance has become a research hotspot. This paper reviews the controllable synthesis strategies of nano-metal-organic frameworks (nano-MOFs) and nano-covalent-organic frameworks (nano-COFs). Firstly, the synthetic evolution of nano-frame materials is summarized. Due to their special surface area, regular pores and adjustable structural functions, nano-frame materials have attracted much attention. Then the preparation methods of nanostructures with different dimensions are introduced. These synthetic strategies provide the basis for the design of novel energy storage and catalytic materials. In addition, the latest advances in the field of energy storage and catalysis are reviewed, with emphasis on the application of nano-MOFs/COFs in zinc-, lithium-, and sodium-based batteries, as well as supercapacitors.

Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform

The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning (DL) using neural networks. The strategy of utilizing neural networks to process output data can reduce the limit of detection (LOD) of the sensor and significantly improve the accuracy (from 93.1%-97.4% to 99%-99.6%). Compared with widely used emerging clinical technologies, our platform achieves accurate decisions with higher sensitivity in a short assay time (∼30 min). The integration of DL models considerably simplifies the readout procedure, resulting in a substantial decrease in processing time. Our findings offer a promising avenue for developing high-precision molecular detection tools for point-of-care (POC) applications.