Engineered 2D materials for optical bioimaging and path toward therapy and tissue engineering.

Two-dimensional (2D) layered materials as a new class of nanomaterial are characterized by a list of exotic properties. These layered materials are investigated widely in several biomedical applications. A comprehensive understanding of the state-of-the-art developments of 2D materials designed for multiple nanoplatforms will aid researchers in various fields to broaden the scope of biomedical applications. Here, we review the advances in 2D material-based biomedical applications. First, we introduce the classification and properties of 2D materials. Next, we summarize surface and structural engineering methods of 2D materials where we discuss surface functionalization, defect, and strain engineering, and creating heterostructures based on layered materials for biomedical applications. After that, we discuss different biomedical applications. Then, we briefly introduced the emerging role of machine learning (ML) as a technological advancement to boost biomedical platforms. Finally, the current challenges, opportunities, and prospects on 2D materials in biomedical applications are discussed.

Monitoring the growth dynamics of colloidal gold-silver core-shell nanoparticles using in situ second harmonic generation and extinction spectroscopy.

The growth dynamics of gold-silver core-shell (Au@Ag) nanoparticles are studied using in situ time-dependent second harmonic generation (SHG) and extinction spectroscopy to investigate the nanoparticle shell formation. The silver shell is grown by reduction of silver cations onto a 14 nm gold core using ascorbic acid in colloidal aqueous solution under varying reaction concentrations producing Au@Ag nanoparticles of final sizes ranging from 51 to 78 nm in diameter. The in situ extinction spectra show a rapid increase in intensity on the timescale of 5-6 s with blue shifting and narrowing of the plasmonic peak during the silver shell formation. The in situ SHG signals show an abrupt rise at early times of the reaction, followed by a time-dependent biexponential decrease, where the faster SHG lifetime corresponds to the timescale of the shell growth, and where the slower SHG lifetime is attributed to changes in the nanoparticle surface charge density. A large enhancement in the SHG signal at early stages of the reaction is caused by plasmonic hot spots due to the nanoparticle surface morphology, which becomes smoother as the reaction proceeds. The final extinction spectra are compared to finite-difference time-domain (FDTD) calculations, showing general agreement with experiment, where the plasmon peak red shifts and increases in spectral width as the silver shell thickness increases. These in situ SHG and extinction spectroscopy results, combined with FDTD calculations, help characterize the complicated processes involved in colloidal nanoparticle shell formation in real time for developing potential plasmon-enhanced nanomaterial applications.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy.

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications.

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.

Understanding the Excitation Wavelength Dependence and Thermal Stability of the SARS-CoV-2 Receptor-Binding Domain Using Surface-Enhanced Raman Scattering and Machine Learning.

COVID-19 has cost millions of lives worldwide. The constant mutation of SARS-CoV-2 calls for thorough research to facilitate the development of variant surveillance. In this work, we studied the fundamental properties related to the optical identification of the receptor-binding domain (RBD) of SARS-CoV-2 spike protein, a key component of viral infection. The Raman modes of the SARS-CoV-2 RBD were captured by surface-enhanced Raman spectroscopy (SERS) using gold nanoparticles (AuNPs). The observed Raman enhancement strongly depends on the excitation wavelength as a result of the aggregation of AuNPs. The characteristic Raman spectra of RBDs from SARS-CoV-2 and MERS-CoV were analyzed by principal component analysis that reveals the role of secondary structures in the SERS process, which is corroborated with the thermal stability under laser heating. We can easily distinguish the Raman spectra of two RBDs using machine learning algorithms with accuracy, precision, recall, and F1 scores all over 95%. Our work provides an in-depth understanding of the SARS-CoV-2 RBD and paves the way toward rapid analysis and discrimination of complex proteins of infectious viruses and other biomolecules.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy.

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aß and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Growth Dynamics of Colloidal Silver-Gold Core-Shell Nanoparticles Studied by In Situ Second Harmonic Generation and Extinction Spectroscopy.

The in situ growth dynamics of colloidal silver-gold core-shell (Ag@Au CS) nanoparticles (NPs) in water are monitored in a stepwise synthesis approach using time-dependent second harmonic generation (SHG) and extinction spectroscopy. Three sequential additions of chloroauric acid, sodium citrate, and hydroquinone are added to the silver nanoparticle solution to grow a gold shell around a silver core. The first addition produces a stable urchin-like surface morphology, while the second and third additions continue to grow the gold shell thickness as the surface becomes more smooth and uniform, as determined using transmission electron microscopy. The extinction spectra after each addition are compared to finite-difference time-domain (FDTD) calculations, showing large deviations for the first and second additions due to the bumpy surface morphology and plasmonic hotspots while showing general agreement after the third addition reaches equilibrium. The in situ SHG signal is dominated by the NP surface, providing complementary information on the growth time scales due to changes to the surface morphology. This combined approach of synthesis and characterization of Ag@Au CS nanoparticles with in situ SHG spectroscopy, extinction spectroscopy, and FDTD calculations provides a detailed foundation for investigating complex colloidal nanoparticle growth mechanisms and dynamics in developing enhanced plasmonic nanomaterial technologies.

Influence of Temperature on Molecular Adsorption and Transport at Liposome Surfaces Studied by Molecular Dynamics Simulations and Second Harmonic Generation Spectroscopy.

A fundamental understanding of the kinetics and thermodynamics of chemical interactions at the phospholipid bilayer interface is crucial for developing potential drug-delivery applications. Here we use molecular dynamics (MD) simulations and surface-sensitive second harmonic generation (SHG) spectroscopy to study the molecular adsorption and transport of a small organic cation, malachite green (MG), at the surface of 1,2-dioleoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (DOPG) liposomes in water at different temperatures. The temperature-dependent adsorption isotherms, obtained by SHG measurements, provide information on adsorbate concentration, free energy of adsorption, and associated changes in enthalpy and entropy, showing that the adsorption process is exothermic, resulting in increased overall entropy. Additionally, the molecular transport kinetics are found to be more rapid under higher temperatures. Corresponding MD simulations are used to calculate the free energy profiles of the adsorption and the molecular orientation distributions of MG at different temperatures, showing excellent agreement with the experimental results.