No Arabic abstract
Glycans play a central role as mediators in most biological processes, but their structures are complicated by isomerism. Epimers and anomers, regioisomers, and branched sequences contribute to a structural variability that dwarfs those of nucleic acids and proteins, challenging even the most sophisticated analytical tools, such as NMR and mass spectrometry. Here, we introduce an electron tunneling technique that is label-free and can identify carbohydrates at the single-molecule level, offering significant benefits over existing technology. It is capable of analyzing sub-picomole quantities of sample, counting the number of individual molecules in each subset in a population of coexisting isomers, and is quantitative over more than four orders of magnitude of concentration. It resolves epimers not well separated by ion-mobility and can be implemented on a silicon chip. It also provides a readout mechanism for direct single-molecule sequencing of linear oligosaccharides.
Homodyne X-ray diffraction signals produced by classical light and classical detectors are given by the modulus square of the charge density in momentum space $left|sigma(mathbf{q})right|^{2}$, missing its phase which is required in order to invert the signal to real space. We show that quantum detection of the radiation field yields a linear diffraction pattern that reveals $sigma(mathbf{q})$ itself, including the phase. We further show that repeated diffraction measurements with variable delays constitute a novel multidimensional measure of spontaneous charge-density fluctuations. Classical diffraction, in contrast, only reveals a subclass of even-order correlation functions. Simulations of two dimensional signals obtained by two diffraction events are presented for the amino acid cysteine.
Visualizing and controlling electron dynamics over femtosecond timescale play a key role in the design of next-generation electronic devices. Using simulations, we demonstrate the electronic oscillation inside the naphthalene molecule can be tracked by means of the tuning of delay time between two identical femtosecond laser pulses. Both the frequency and decay time of the oscillation are detected by the tunneling charge through the junction of scanning tunneling microscopy. And the tunneling charge is sensitive to the carrier-envelope phase (CEP) for few-cycle long optical pulses. While this sensitivity to CEP will disappear with the increase of time-length of pulses. Our simulation results show that it is possible to visualize and control the electron dynamics inside the molecule by one or two femtosecond laser pulses.
The strong spatial confinement of a nanocavity plasmonic field has made it possible to visualize the inner structure of a single molecule and even to distinguish its vibrational modes in real space. With such ever-improved spatial resolution, it is anticipated that full vibrational imaging of a molecule could be achieved to reveal molecular structural details. Here we demonstrate full Raman images of individual vibrational modes on the {AA}ngstrom level for a single Mg-porphine molecule, revealing distinct characteristics of each vibrational mode in real space. Furthermore, by exploiting the underlying interference effect and Raman fingerprint database, we propose a new methodology for structural determination, coined as scanning Raman picoscopy, to show how such ultrahigh-resolution spectromicroscopic vibrational images can be used to visually assemble the chemical structure of a single molecule through a simple Lego-like building process.
The structure of Fenna-Matthews-Olson (FMO) light-harvesting complex has long been recognized as containing seven bacteriochlorophyll (BChl) molecules. Recently, an additional BChl molecule was discovered in the crystal structure of the FMO complex, which may serve as a link between baseplate and the remaining seven molecules. Here, we investigate excitation energy transfer (EET) process by simulating single-molecule pump-dump experiment in the eight-molecules complex. We adopt the coherent modified Redfield theory and non-Markovian quantum jump method to simulate EET dynamics. This scheme provides a practical approach of detecting the realistic EET pathway in BChl complexes with currently available experimental technology. And it may assist optimizing design of artificial light-harvesting devices.
A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra, as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ${sim}$83,000 unique organic molecules (between 18 and 180 amu) optimized at the $omega$B97X-D/6-31+G(d) level of theory where the theoretical uncertainty of the spectroscopic constants are well understood and used to further augment training. Since chemical and structural properties depend highly on molecular composition, we divided the dataset into four groups corresponding to pure hydrocarbons, oxygen-bearing, nitrogen-bearing, and both oxygen- and nitrogen-bearing species, training each type of network with one of these categories thus creating experts within each domain of molecules. We demonstrate how these models can then be used for practical inference on four molecules, and discuss both the strengths and shortcomings of our approach, and the future directions these architectures can take.