Various graphite additives were incorporated into the positive paste in a range of amounts to study and compare their effects on the positive active mass utilization of lead-acid batteries. Four types of graphite—two anisotropic, one globular, and one fibrous—were investigated by SEM, XRD, and Raman spectroscopy. Their physico-chemical properties were correlated to the electrochemical performances of 2 V test batteries under a wide range of conditions. This works presents the influence of graphite additives’ structural order, phase composition, particle size, morphology, and surface area on the formation, initial cycling, and electrochemical utilization of the positive plate. The effects of various graphite on electrochemical performance were investigated using SEM, mercury porosimetry, and TGA/DSC to correlate the function of graphite on the positive active mass utilization of the lead-acid battery.
The effects of expanded and not expanded (natural flake) graphite additives were evaluated on the discharge utilization of the positive active material (PAM) in the lead-acid battery. Graphite powders were added to the paste at 2.20 vol. % and tested in model 2V battery cells under a wide range of discharge currents from 8C to C/20. The effects of graphite on the PAM pore volume and pore size distribution were measured with mercury porosimetry, and a good correlation was found between the pore volume of the PAM and utilization performance of the cells. It was shown that the powder characteristics of graphite can affect the PAM pore volume. A correlation was found between the graphite additives’ structural order and PAM utilization.
The accuracy and ease of metabolite assignments from
a complex
mixture are expected to be facilitated by employing a multispectral
approach. The two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) and 2D 1H–1H–total correlation spectroscopy (TOCSY)
are the experiments commonly used for metabolite assignments. The
2D 1H–13C HSQC–TOCSY and 2D 1H–13C heteronuclear multiple-bond correlation
(HMBC) are routinely used by natural products chemists but have seen
minimal usage in metabolomics despite the unique information, the
nearly complete 1H–1H and 1H–13C and spin systems provided by these experiments
that may improve the accuracy and reliability of metabolite assignments.
The use of a 13C-labeled feedstock such as glucose is a
routine practice in metabolomics to improve sensitivity and to emphasize
the detection of specific metabolites but causes severe artifacts
and an increase in spectral complexity in the HMBC experiment. To
address this issue, the standard HMBC pulse sequence was modified
to include carbon decoupling. Nonuniform sampling was also employed
for rapid data collection. A dataset of reference 2D 1H–13C HMBC spectra was collected for 94 common metabolites. 13C–13C spin connectivity was then obtained
by generating a covariance pseudo-spectrum from the carbon-decoupled
HMBC and the 1H–13C HSQC–TOCSY
spectra. The resulting 13C–13C pseudo-spectrum
provides a connectivity map of the entire carbon backbone that uniquely
describes each metabolite and would enable automated metabolite identification.
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