Air-stable solid electrolytes, possessing good Li-ion conductivity and facilitating low resistance at electrode/electrolyte interface, are essential towards facile development/handling of safe-cum-stable solid-state Li-ion batteries, possessing desired power densities. In this context, we report here the design and development of Al/Mg co-doped Li-La-zirconate (LLZO) based solid electrolytes that address the concerns associated with inferior air stability of Al-doped LLZO, while retaining Li-ion conductivity (∼4.7 × 10 −4 s cm −1 ) as good as that for similarly developed Al-doped LLZO and higher than that for Mg-doped counterpart. Doping as "little" as 0.1 mole of Mg-ion (per mole of LLZO), as for the optimized Al(0.2)/Mg(0.1) co-doped LLZO, is sufficient to impart long term phase and structural stability in air, unlike that for Al-doped LLZO. Accordingly, Li-ion conductivity of Al(0.2)/ Mg(0.1) co-doped LLZO gets nearly retained even after air-exposure for 50 days, in contrast to lowering of conductivity for Aldoped LLZO by ∼3 orders of magnitude after 24 days. The influence of excellent air stability and Li-ion conductivity could be seen on the Li/LLZO interfacial resistance, with the Al(0.2)/Mg(0.1) co-doped LLZO possessing the lowest area specific resistance of ∼18 Ω cm 2 among those investigated, which is also among the lowest reported to-date, without any additional surface/interfacial engineering being done.
Background Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. Results Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia.
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