Dataset Information

Vibrational nano-spectroscopic imaging correlating structure with intermolecular coupling and dynamics.

ABSTRACT: Molecular self-assembly, the function of biomembranes and the performance of organic solar cells rely on nanoscale molecular interactions. Understanding and control of such materials have been impeded by difficulties in imaging their properties with the desired nanometre spatial resolution, attomolar sensitivity and intermolecular spectroscopic specificity. Here we implement vibrational scattering-scanning near-field optical microscopy with high spectral precision to investigate the structure-function relationship in nano-phase separated block copolymers. A vibrational resonance is used as a sensitive reporter of the local chemical environment and we image, with few nanometre spatial resolution and 0.2 cm(-1) spectral precision, solvatochromic Stark shifts and line broadening correlated with molecular-scale morphologies. We discriminate local variations in electric fields between nano-domains with quantitative agreement with dielectric continuum models. This ability to directly resolve nanoscale morphology and associated intermolecular interactions can form a basis for the systematic control of functionality in multicomponent soft matter systems.

SUBMITTER: Pollard B

PROVIDER: S-EPMC4071972 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.

Dataset Information

Vibrational nano-spectroscopic imaging correlating structure with intermolecular coupling and dynamics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets