arXiv subjects

Anthony J. Dominic III

Publications and source records attributed to Anthony J. Dominic III.

2 recordsLinked to original sources

arXiv2026

Short-lived memory in multidimensional spectra encodes full signal evolution

Ultrafast multidimensional spectroscopies are powerful tools that can access charge and energy flow in complex materials, shifting chemical kinetics, and even many-body interactions in correlated matter. However, current implementations typically involve complex apparatuses and long averaging times. As a result, these methods have been limited to detailed mechanistic investigations of a few samples, precluding the broad characterization of molecular systems and/or the optimization of material ones. For example, converging the statistical noise in 2D spectra becomes exponentially expensive with increasing waiting times, and extended laser exposure heightens the probability of sample degradation. We address this fundamental challenge by developing a new technique, the spectral generalized master equation (GME), that enables one to employ short-waiting time 2D spectra to determine the full evolution of 2D spectra over arbitrary waiting times with high temporal resolution. In addition to reducing the cost of experiments by multiple orders of magnitude, our approach accurately removes statistical noise, reducing the need for time averaging, while circumventing the increasing convergence costs with longer waiting times. We provide a rigorous theoretical footing for the spectral GME and demonstrate its applicability on theoretically generated and experimentally measured 2D electronic and 2D infrared spectra. We anticipate that this advance has the potential to enable the investigation of systems that are too delicate for study with current multidimensional spectroscopies and accelerate the progress of 2D spectroscopy-based microscopies that can offer highly resolved excitation dynamics with spatial resolution over heterogeneous environments.

physics.chem-ph↗

arXiv2024

A pedagogical tour of the Fourier transform with applications to NMR and IR spectroscopy

The Fourier Transform (FT) is a fundamental tool that permeates modern science and technology. While chemistry undergraduates encounter the FT as early as second year, their courses often only mention it in passing because computers frequently perform it automatically behind the scenes. Although this automation enables students to focus on `the chemistry', students miss out on an opportunity to understand and use one of the most powerful tools in the scientific arsenal capable of revealing how time-dependent signals encode chemical structure. Although many educational resources introduce chemists to the FT, they often require familiarity with sophisticated mathematical and computational concepts. Here, we present a series of three self-contained, Python-based laboratory activities for undergraduates to understand the FT and apply it to analyze audio signals, an infrared (IR) spectroscopy interferogram, and a nuclear magnetic resonance (NMR) free induction decay (FID). In these activities, students observe how the FT reveals and quantifies the contribution of each frequency present in a temporal signal and how decay timescales dictate signal broadening. Our activities empower students with the tools to transform their own temporal datasets (e.g., FID) to a frequency spectrum. To ensure accessibility of the activities and lower the barrier to implementation, we utilize Google Colab's open-source, cloud-based platform to run Jupyter notebooks. We also offer a pre-laboratory activity that introduces students to the basics of Python and the Colab platform, and reviews the math and programming skills needed to complete the lab activities. These lab activities help students build a qualitative, quantitative, and practical understanding of the FT.

physics.ed-ph↗