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Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m2/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H2SO4. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

Most lithium batteries offer a wide range of applications. However, safety issues are still an unresolved issue for several applications. To solve the safety issue of Li-ion batteries, solid polymer electrolyte is a promising candidate to replace commercial liquid electrolyte. A 4-arm star poly(ethylene oxide) polymer with LiTFSI salt as an electrolyte was studied. The dynamics of this polymer were explored with the Quasi-Elastic Neutron Scattering technique. Furthermore, the influence of temperature and Li salt concentration on the polymer dynamics was investigated. The dynamics of the polymer ends of the arm show much higher flexibility than the core parts making those types of polymers attractive for further studies in battery research.

A recently proposed wide-angle velocity selector (WAVES) device for choosing the velocity of detected neutrons after they have been scattered by the sample paves the way for inverted geometry neutron spectrometers with continuously adjustable final neutron wavelength. BWAVES broadband inverted geometry spectrometer proposed for the Second Target Station at the Spallation Neutron Source at Oak Ridge National Laboratory is designed using WAVES to simultaneously probe dynamic processes spanning 4.5 decades in time (energy transfer). This makes BWAVES a uniquely flexible instrument which can be viewed as either a quasielasitc neutron scattering (QENS) spectrometer with a practically unlimited (overlapping with the vibrational excitations) range of energy transfers, or a broadband inelastic vibrational neutron spectrometer with QENS capabilities, including a range of accessible momentum transfer (Q) and a sufficiently high energy resolution at the elastic line. The new capabilities offered by BWAVES will expand the application of neutron scattering in ways not possible with existing neutron spectrometers.

Temperature-dependent scans of the neutron scattering intensity are commonly employed in high energy-resolution quasielastic measurements. Besides serving as a useful diagnostic tool for identifying the temperature range that could give rise to a measurable relaxation signal, such scans of the “elastic” (resolution-defined) intensity could be employed for determining the temperature at which the relaxation time in the system becomes equal to the resolution-defined characteristic time of the spectrometer measurement. This is a model-independent alternative to the “traditional” approach, when, at a given measurement temperature, the relaxation time in the system is obtained from fitting the full dynamic spectra with a model scattering function. Here we introduce the temperature-dependent scan of the neutron spin-echo signal amplitude. Using a well-characterized system with a complex relaxation pattern, we demonstrate that the relaxation time obtained from the approach proposed herein maps well on the previous “traditionally” measured relaxation times. Thus, despite monitoring a different variable (neutron spin-echo signal amplitude vs. neutron scattering intensity), the benefits of the model-free temperature-dependent scan approach, traditionally utilized in neutron time-of-flight and backscattering experiments, can be extended to measurements of the very slow relaxations assessable only by high-resolution neutron spin-echo.

ICE-MAN is a modeling and analysis workbench for multi-modal studies, designed with neutron science in mind. It streamlines the workflow between different experimental techniques, computer modeling, and databases and reduces the time and learning curve needed to access them thus making a holistic approach to data interpretation more amenable and efficient.