In situ second harmonic generation (SHG) coupled with extinction spectroscopy is used for real-time monitoring of seed-mediated growth dynamics of colloidal citrate-stabilized gold nanoparticles in water. The time-dependent in situ SHG results capture an early stage of the growth process where a large enhancement in the SHG signal is observed, which is attributed to the formation of plasmonic hot spots from a rough and uneven nanoparticle surface. The temporal peak in the SHG signal is followed by a decay that is fit to an exponential function to characterize the size-dependent nanoparticle growth lifetime, which varies from 0.45 to 1.7 min for final nanoparticle sizes of 66 and 94 nm, respectively. This early growth stage also corresponds to a broadening of the plasmon spectra, as monitored using timedependent in situ extinction spectroscopy. Over the course of the seed-mediated growth reaction, the nanoparticle becomes more thermodynamically stable through surface reconstruction resulting in a smoother, more uniform surface, corresponding to lower, stable SHG signals and narrower plasmon spectra. With real-time monitoring of nanoparticle formation, in situ SHG spectroscopy combined with in situ extinction spectroscopy provides an important insight for controlling nanoparticle synthesis and surface morphology for potential nanoscale engineering of different colloidal nanomaterials.

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.

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.

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 Aβ 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.

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.