High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.

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The capability of battery materials to deliver not only high lithium storage capacity, but also the ability to operate at high charge/discharge rates is an essential property for development of new batteries. In the present work, the influence on the charge/discharge rate behaviour of substoichiometric concentrations of phosphorus (P) in silicon (Si) nanoparticles was studied. The results revealed an increase in rate capability as a function of the P concentration between 0 and 5.2 at %, particularly during delithiation. The stoichiometry of the nano-particles was found to strongly affect the formation of the Li 3.5 Si phase during lithiation. Cyclic stability experiments demonstrated an initial increase in capacity for the SiP x materials. Galvanostatic intermittent titration technique and electrochemical impedance spectroscopy demonstrated the increased lithium diffusivity with inclusion of P. Density functional theory and ab initio molecular dynamics were deployed to provide a rationale for the electrochemical behaviour of SiP x .

Silicon (Si) is an important anode material for lithium ion batteries (LIBs), and increasing the loading of Si electrodes is an important step towards commercialization. However, half cells commonly used...