Toward the Next Frontiers of Vibrational Bioimaging

The Duality of Raman Scattering

ConspectusFirst predicted more than 100 years ago, Raman scattering is a cornerstone of photonics, spectroscopy, and imaging. The conventional framework of understanding Raman scattering was built on Raman cross section σRaman. Carrying a dimension of area, σRaman characterizes the interaction strength between light and molecules during inelastic scattering. The numerical values of σRaman turn out to be many orders of magnitude smaller in comparison to the linear absorption cross sections σAbsorption of similar molecular systems. Such an enormous gap has been the reason for researchers to believe the extremely feeble Raman scattering ever since its discovery. However, this prevailing picture is conceptually problematic or at least incomplete due to the fact that Raman scattering and linear absorption belong to different orders of light-matter interaction.In this Account, we will summarize an alternate way to think about Raman scattering, which we term stimulated response formulation. To capture the third-order interaction nature of Raman scattering, we introduced stimulated Raman cross section, σSRS, defined as the intrinsic molecular property in response to the external photon fluxes. Foremost, experimental measurement of σSRS turns out to be not weak at all or even larger when fairly compared with electronic counterparts of the same order. The analytical expression for σSRS derived from quantum electrodynamics also supports the measurement and proves that σSRS is intrinsically strong. Hence, σRaman and σSRS can be extremely small and large, respectively, for the same molecule at the same time. Our subsequent theoretical studies show that stimulated response formulation can unify spontaneous emission, stimulated emission, spontaneous Raman, and stimulated Raman via eq 10, in a coherent and symmetric way. In particular, an Einstein-coefficient-like equation, eq 12a, was derived, showing that σRaman can be explicitly expressed as σSRS multiplied by an effective photon flux arising from zero-point fluctuation of the vacuum. The feeble vacuum fluctuation hence explains how σSRS can be intrinsically strong while, at the same time, σRaman ends up being many orders of magnitude smaller when both compared to the electronic counterparts. These two sides of the same coin prompted us to propose "the duality of Raman scattering" (Table 1). Finally, this formulation naturally leads to a quantitative treatment of stimulated Raman scattering (SRS) microscopy, providing an intuitive, molecule-centric explanation as to how SRS microscopy can outperform regular Raman microscopy. Hence, as unveiled by the new formulation, a duality of Raman scattering has emerged, with implications for both fundamental science and practical technology.

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Atomic Force Microscopy-Based Nanoscale Infrared Techniques for Catalysis

Atomic force microscopy (AFM)-based nanoscale infrared (nano-IR) techniques have found extensive application in the fields of chemistry, physics, and materials science, enabling the visualization of nanoscale features that surpass the optical diffraction limit. More recently, tentative investigations have been conducted into the use of these techniques in the field of catalysis, particularly in studying interfacial processes involving molecular monolayer samples. IR nanoimaging and nanospectroscopy offer unique perspectives on catalytic processes. Considering the specific characteristics of catalytic processes, this Perspective highlights the need for and reviews the current status of AFM-based nano-IR techniques for catalysis investigations, which aims to contribute to a deeper understanding of the nanoscale mechanisms underlying the catalytic processes.