Predicting System Degradation with a Guided Neural Network Approach.

Evaluating the physical degradation behavior and estimating the lifetime of engineering systems and structures is crucial to ensure their safe and reliable operation. However, measuring lifetime through actual operating conditions can be a difficult and slow process. While valuable and quick in measuring lifetimes, accelerated life testing is often oversimplified and does not provide accurate simulations of the exact operating environment. This paper proposes a data-driven framework for time-efficient modeling of field degradation using sensor measurements from short-term actual operating conditions degradation tests. The framework consists of two neural networks: a physics discovery neural network and a predictive neural network. The former models the underlying physics of degradation, while the latter makes probabilistic predictions for degradation intensity. The physics discovery neural network guides the predictive neural network for better life estimations. The proposed framework addresses two main challenges associated with applying neural networks for lifetime estimation: incorporating the underlying physics of degradation and requirements for extensive training data. This paper demonstrates the effectiveness of the proposed approach through a case study of atmospheric corrosion of steel test samples in a marine environment. The results show the proposed framework's effectiveness, where the mean absolute error of the predictions is lower by up to 76% compared to a standard neural network. By employing the proposed data-driven framework for lifetime prediction, systems safety and reliability can be evaluated efficiently, and maintenance activities can be optimized.

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management.

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

Self-Assembly of Hybrid Nanorods for Enhanced Volumetric Performance of Nanoparticles in Li-Ion Batteries.

The benefits of nanosize active particles in Li-ion batteries are currently ambiguous. They are acclaimed for enhancing the cyclability of certain electrode materials and for improving rate performance. However, at the same time, nanoparticles are criticized for causing side reactions as well as for their low packing density and, therefore, poor volumetric battery performance. This paper demonstrates for the first time that self-assembly can be used to pack nanoparticles into dense battery electrodes with up to 4-fold higher volumetric capacities. Furthermore, despite the dense packing of the self-assembled electrodes, they retain a higher volumetric capacity than randomly dispersed nanoparticles up to rates of 5 C. Finally, we did not observe substential degradation in capacity after 1000 cycles, and post-mortem analysis indicates that the self-assembled structures are maintained during cycling. Therefore, the proposed self-assembled electrodes profit from the advantages of nanostructured battery materials without compromising the volumetric performance.

A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics.

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system's reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.

Measures of Entropy to Characterize Fatigue Damage in Metallic Materials.

This paper presents the entropic damage indicators for metallic material fatigue processes obtained from three associated energy dissipation sources. Since its inception, reliability engineering has employed statistical and probabilistic models to assess the reliability and integrity of components and systems. To supplement the traditional techniques, an empirically-based approach, called physics of failure (PoF), has recently become popular. The prerequisite for a PoF analysis is an understanding of the mechanics of the failure process. Entropy, the measure of disorder and uncertainty, introduced from the second law of thermodynamics, has emerged as a fundamental and promising metric to characterize all mechanistic degradation phenomena and their interactions. Entropy has already been used as a fundamental and scale-independent metric to predict damage and failure. In this paper, three entropic-based metrics are examined and demonstrated for application to fatigue damage. We collected experimental data on energy dissipations associated with fatigue damage, in the forms of mechanical, thermal, and acoustic emission (AE) energies, and estimated and correlated the corresponding entropy generations with the observed fatigue damages in metallic materials. Three entropic theorems-thermodynamics, information, and statistical mechanics-support approaches used to estimate the entropic-based fatigue damage. Classical thermodynamic entropy provided a reasonably constant level of entropic endurance to fatigue failure. Jeffreys divergence in statistical mechanics and AE information entropy also correlated well with fatigue damage. Finally, an extension of the relationship between thermodynamic entropy and Jeffreys divergence from molecular-scale to macro-scale applications in fatigue failure resulted in an empirically-based pseudo-Boltzmann constant equivalent to the Boltzmann constant.

Fully Adaptive Particle Filtering Algorithm for Damage Diagnosis and Prognosis.

A fully adaptive particle filtering algorithm is proposed in this paper which is capable of updating both state process models and measurement models separately and simultaneously. The approach is a significant step toward more realistic online monitoring or tracking damage. The majority of the existing methods for Bayes filtering are based on predefined and fixed state process and measurement models. Simultaneous estimation of both state and model parameters has gained attention in recent literature. Some works have been done on updating the state process model. However, not many studies exist regarding an update of the measurement model. In most of the real-world applications, the correlation between measurements and the hidden state of damage is not defined in advance and, therefore, presuming an offline fixed measurement model is not promising. The proposed approach is based on optimizing relative entropy or Kullback-Leibler divergence through a particle filtering algorithm. The proposed algorithm is successfully applied to a case study of online fatigue damage estimation in composite materials.

The use of the integrated cognitive assessment to improve the efficiency of primary care referrals to memory services in the accelerating dementia pathway technologies study.

Background: Current primary care cognitive assessment tools are either crude or time-consuming instruments that can only detect cognitive impairment when it is well established. This leads to unnecessary or late referrals to memory services, by which time the disease may have already progressed into more severe stages. Due to the COVID-19 pandemic, some memory services have adapted to the new environment by shifting to remote assessments of patients to meet service user demand. However, the use of remote cognitive assessments has been inconsistent, and there has been little evaluation of the outcome of such a change in clinical practice. Emerging research has highlighted computerized cognitive tests, such as the Integrated Cognitive Assessment (ICA), as the leading candidates for adoption in clinical practice. This is true both during the pandemic and in the post-COVID-19 era as part of healthcare innovation. Objectives: The Accelerating Dementias Pathways Technologies (ADePT) Study was initiated in order to address this challenge and develop a real-world evidence basis to support the adoption of ICA as an inexpensive screening tool for the detection of cognitive impairment and improving the efficiency of the dementia care pathway. Methods: Ninety-nine patients aged 55-90 who have been referred to a memory clinic by a general practitioner (GP) were recruited. Participants completed the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome were compared with the specialist diagnosis obtained at the memory clinic.Participants were given the option to carry out a retest visit where they were again given the chance to take the ICA test either remotely or face-to-face. Results: The primary outcome of the study compared GP referral with specialist diagnosis of mild cognitive impairment (MCI) and dementia. Of those the GP referred to memory clinics, 78% were necessary referrals, with ~22% unnecessary referrals, or patients who should have been referred to other services as they had disorders other than MCI/dementia. In the same population the ICA was able to correctly identify cognitive impairment in ~90% of patients, with approximately 9% of patients being false negatives. From the subset of unnecessary GP referrals, the ICA classified ~72% of those as not having cognitive impairment, suggesting that these unnecessary referrals may not have been made if the ICA was in use. ICA demonstrated a sensitivity of 93% for dementia and 83% for MCI, with a specificity of 80% for both conditions in detecting cognitive impairment. Additionally, the test-retest prediction agreement for the ICA was 87.5%. Conclusion: The results from this study demonstrate the potential of the ICA as a screening tool, which can be used to support accurate referrals from primary care settings, along with the work conducted in memory clinics and in secondary care. The ICA's sensitivity and specificity in detecting cognitive impairment in MCI surpassed the overall standard of care reported in existing literature.

Health economic analysis of the integrated cognitive assessment tool to aid dementia diagnosis in the United Kingdom.

Objectives: The aim of this study was to develop a comprehensive economic evaluation of the integrated cognitive assessment (ICA) tool compared with standard cognitive tests when used for dementia screening in primary care and for initial patient triage in memory clinics. Methods: ICA was compared with standard of care comprising a mixture of cognitive assessment tools over a lifetime horizon and employing the UK health and social care perspective. The model combined a decision tree to capture the initial outcomes of the cognitive testing with a Markov structure that estimated long-term outcomes of people with dementia. Quality of life outcomes were quantified using quality-adjusted life years (QALYs), and the economic benefits were assessed using net monetary benefit (NMB). Both costs and QALYs were discounted at 3.5% per annum and cost-effectiveness was assessed using a threshold of £20,000 per QALY gained. Results: ICA dominated standard cognitive assessment tools in both the primary care and memory clinic settings. Introduction of the ICA tool was estimated to result in a lifetime cost saving of approximately £123 and £226 per person in primary care and memory clinics, respectively. QALY gains associated with early diagnosis were modest (0.0016 in primary care and 0.0027 in memory clinic). The net monetary benefit (NMB) of ICA introduction was estimated at £154 in primary care and £281 in the memory clinic settings. Conclusion: Introduction of ICA as a tool to screen primary care patients for dementia and perform initial triage in memory clinics could be cost saving to the UK public health and social care payer.

Brain Representation of Animal and Non-Animal Images in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

Background: In early Alzheimer's disease (AD), high-level visual functions and processing speed are impacted. Few functional magnetic resonance imaging (fMRI) studies have investigated high-level visual deficits in AD, yet none have explored brain activity patterns during rapid animal/non-animal categorization tasks. Objective: To address this, we utilized the previously known Integrated Cognitive Assessment (ICA) to collect fMRI data and compare healthy controls (HC) to individuals with mild cognitive impairment (MCI) and mild AD. Methods: The ICA encompasses a rapid visual categorization task that involves distinguishing between animals and non-animals within natural scenes. To comprehensively explore variations in brain activity levels and patterns, we conducted both univariate and multivariate analyses of fMRI data. Results: The ICA task elicited activation across a range of brain regions, encompassing the temporal, parietal, occipital, and frontal lobes. Univariate analysis, which compared responses to animal versus non-animal stimuli, showed no significant differences in the regions of interest (ROIs) across all groups, with the exception of the left anterior supramarginal gyrus in the HC group. In contrast, multivariate analysis revealed that in both HC and MCI groups, several regions could differentiate between animals and non-animals based on distinct patterns of activity. Notably, such differentiation was absent within the mild AD group. Conclusions: Our study highlights the ICA task's potential as a valuable cognitive assessment tool designed for MCI and AD. Additionally, our use of fMRI pattern analysis provides valuable insights into the complex changes in brain function associated with AD. This approach holds promise for enhancing our understanding of the disease's progression.

The Use of a Computerized Cognitive Assessment to Improve the Efficiency of Primary Care Referrals to Memory Services: Protocol for the Accelerating Dementia Pathway Technologies (ADePT) Study.

BACKGROUND: Existing primary care cognitive assessment tools are crude or time-consuming screening instruments which can only detect cognitive impairment when it is well established. Due to the COVID-19 pandemic, memory services have adapted to the new environment by moving to remote patient assessments to continue meeting service user demand. However, the remote use of cognitive assessments has been variable while there has been scant evaluation of the outcome of such a change in clinical practice. Emerging research in remote memory clinics has highlighted computerized cognitive tests, such as the Integrated Cognitive Assessment (ICA), as prominent candidates for adoption in clinical practice both during the pandemic and for post-COVID-19 implementation as part of health care innovation. OBJECTIVE: The aim of the Accelerating Dementia Pathway Technologies (ADePT) study is to develop a real-world evidence basis to support the adoption of ICA as an inexpensive screening tool for the detection of cognitive impairment to improve the efficiency of the dementia care pathway. METHODS: Patients who have been referred to a memory clinic by a general practitioner (GP) are recruited. Participants complete the ICA either at home or in the clinic along with medical history and usability questionnaires. The GP referral and ICA outcome are compared with the specialist diagnosis obtained at the memory clinic. The clinical outcomes as well as National Health Service reference costing data will be used to assess the potential health and economic benefits of the use of the ICA in the dementia diagnosis pathway. RESULTS: The ADePT study was funded in January 2020 by Innovate UK (Project Number 105837). As of September 2021, 86 participants have been recruited in the study, with 23 participants also completing a retest visit. Initially, the study was designed for in-person visits at the memory clinic; however, in light of the COVID-19 pandemic, the study was amended to allow remote as well as face-to-face visits. The study was also expanded from a single site to 4 sites in the United Kingdom. We expect results to be published by the second quarter of 2022. CONCLUSIONS: The ADePT study aims to improve the efficiency of the dementia care pathway at its very beginning and supports systems integration at the intersection between primary and secondary care. The introduction of a standardized, self-administered, digital assessment tool for the timely detection of neurodegeneration as part of a decision support system that can signpost accordingly can reduce unnecessary referrals, service backlog, and assessment variability. TRIAL REGISTRATION: ISRCTN 16596456; https://www.isrctn.com/ISRCTN16596456. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34475.

Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment.

Electroencephalography (EEG) has been commonly used to measure brain alterations in Alzheimer's Disease (AD). However, reported changes are limited to those obtained from using univariate measures, including activation level and frequency bands. To look beyond the activation level, we used multivariate pattern analysis (MVPA) to extract patterns of information from EEG responses to images in an animacy categorization task. Comparing healthy controls (HC) with patients with mild cognitive impairment (MCI), we found that the neural speed of animacy information processing is decreased in MCI patients. Moreover, we found critical time-points during which the representational pattern of animacy for MCI patients was significantly discriminable from that of HC, while the activation level remained unchanged. Together, these results suggest that the speed and pattern of animacy information processing provide clinically useful information as a potential biomarker for detecting early changes in MCI and AD patients.

NREM sleep staging using WAV(CNS) index.

OBJECTIVE: Visual scoring of 30-s epochs of sleep data is not always adequate to show the dynamic structure of sleep in sufficient details. It is also prone to considerable inter- and intra-rater variability. Moreover, it involves considerable training and experience, and is very tedious, time-consuming, labor-intensive and costly. Hence, automatic sleep staging is needed to overcome these limitations. Since naturally occurring NREM sleep and anesthesia have been reported to possess various underlying neurophysiological similarities, EEG-based depth-of-anesthesia monitors have started to penetrate into sleep research. This study investigates the ability of WAV(CNS) index (as implemented in NeuroSENSE depth-of-anesthesia monitor) to detect NREM sleep stages and wake state for full overnight PSG data. METHODS: Full overnight PSG sleep data, obtained from 24 adolescents, was scored by a registered PSG technologist for different sleep stages. Retrospective analysis was performed on a single frontal channel using the WAV(CNS) algorithm. Non-parametric descriptive statistics were used to examine the relationship between WAV(CNS) index and sleep stages. RESULTS: A strong correlation (ρ = 0.9458) was found between the WAV(CNS) index and NREM sleep stages, with WAV(CNS) index values decreasing with increasing sleep stages. Moreover, there was no significant overlap between different NREM sleep stages as classified by the WAV(CNS) index, which was able to significantly differentiate (P < 0.001) between all pairs of Awake and different NREM stages. CONCLUSIONS: This study demonstrates that changes in the depth of natural NREM sleep are reflected sensitively by changes in the WAV(CNS) index. Hence, WAV(CNS) index may serve as an automatic real-time indicator of depth of natural sleep with high temporal resolution, and can possibly be of great use for automated sleep staging in routine/postoperative somnographic studies.

Validity and Cultural Generalisability of a 5-Minute AI-Based, Computerised Cognitive Assessment in Mild Cognitive Impairment and Alzheimer's Dementia.

Introduction: Early detection and monitoring of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) patients are key to tackling dementia and providing benefits to patients, caregivers, healthcare providers and society. We developed the Integrated Cognitive Assessment (ICA); a 5-min, language independent computerised cognitive test that employs an Artificial Intelligence (AI) model to improve its accuracy in detecting cognitive impairment. In this study, we aimed to evaluate the generalisability of the ICA in detecting cognitive impairment in MCI and mild AD patients. Methods: We studied the ICA in 230 participants. 95 healthy volunteers, 80 MCI, and 55 mild AD participants completed the ICA, Montreal Cognitive Assessment (MoCA) and Addenbrooke's Cognitive Examination (ACE) cognitive tests. Results: The ICA demonstrated convergent validity with MoCA (Pearson r=0.58, p<0.0001) and ACE (r=0.62, p<0.0001). The ICA AI model was able to detect cognitive impairment with an AUC of 81% for MCI patients, and 88% for mild AD patients. The AI model demonstrated improved performance with increased training data and showed generalisability in performance from one population to another. The ICA correlation of 0.17 (p = 0.01) with education years is considerably smaller than that of MoCA (r = 0.34, p < 0.0001) and ACE (r = 0.41, p < 0.0001) which displayed significant correlations. In a separate study the ICA demonstrated no significant practise effect over the duration of the study. Discussion: The ICA can support clinicians by aiding accurate diagnosis of MCI and AD and is appropriate for large-scale screening of cognitive impairment. The ICA is unbiased by differences in language, culture, and education.

Author Correction: The first annotated set of scanning electron microscopy images for nanoscience.

Following further analysis of the Majority Dataset (Data Citation 3, originally https://doi.org/10.23728/b2share.e344a8afef08463a855ada08aadbf352 ) and 100% Dataset (Data Citation 4, originally https://doi.org/10.23728/b2share.f1aa0f5ad38c456eaf7b04d47a65af53 ) presented in the original version of this Data Descriptor it was revealed that a large number of duplicate images were included in both datasets. Both datasets have been corrected in updated versions, removing all replicates. The new version of the Majority Dataset (Data Citation 3) can be accessed via https://doi.org/10.23728/b2share.72758204db9044ab8b3e6b6c4d2eb576 and the 100% Dataset (Data Citation 4) via https://doi.org/10.23728/b2share.80df8606fcdb4b2bae1656f0dc6db8ba . The HTML and PDF versions of the Data Descriptor have been corrected accordingly.

Triple-Cation-Based Perovskite Photocathodes with AZO Protective Layer for Hydrogen Production Applications.

Metal halide perovskites are actively pursued as photoelectrodes to drive solar fuel synthesis. However, currently, these photocathodes suffer from limited stability in water, which hampers their practical application. Here, we report a high-performance solution-processable photocathode composed of cesium formamidinium methylammonium triple-cation lead halide perovskite protected by an Al-doped ZnO (AZO) layer combined with a Field's metal encapsulation. Careful selection of charge transport layers resulted in an improvement in photocurrent, fill factor, device stability and reproducibility. The dead pixels count reduced from 25 to 6% for the devices with an AZO layer, and in photocathodes with an AZO layer the photocurrent density increased by almost 20% to 14.3 mA cm-2. In addition, we observed a 5-fold increase in the device lifetime for photocathodes with AZO, which reached up to 18 h before complete failure. Finally, the photocathodes are fabricated using low-cost and scalable methods, which have promise to become compatible with standard solution-based processes.

A measure of ventilatory variability at wake-sleep transition predicts sleep apnea severity.

RATIONALE: Increased variability in ventilation may contribute to the pathogenesis of obstructive sleep apnea (OSA) by promoting ventilatory instability, fluctuations of neuromuscular output to the upper airway, and pharyngeal collapsibility. We assessed the association of a measure of ventilatory variability measured at the wake-sleep transition with OSA and associated covariates. METHODS: Four hundred eighty-five participants in the Cleveland Family Study underwent overnight polysomnography with independent derivation of the ventilatory variability index (VVI) and the apnea-hypopnea index (AHI). The VVI was calculated from the variability in the power spectrum of the abdominal inductance signal over a 2-min period beginning at sleep onset. RESULTS: The VVI was strongly correlated with the AHI (r=0.43; p<0.001). After adjusting for age, body mass index, sex, and race, the VVI remained significantly associated with AHI (p<0.001). The adjusted odds ratio for OSA (AHI, >or=15) with each half SD increase in VVI was 1.41 (range, 1.25 to 1.59). In a subgroup analysis of obese snorers, to limit analyses to those with a presumed anatomic predisposition for apnea, VVI remained associated with an elevated AHI. CONCLUSIONS: Increased ventilatory variability may be a useful phenotype in characterizing OSA.

The first annotated set of scanning electron microscopy images for nanoscience.

In this paper, we present the first publicly available human-annotated dataset of images obtained by the Scanning Electron Microscopy (SEM). A total of roughly 26,000 SEM images at the nanoscale are classified into 10 categories to form 4 labeled training sets, suited for image recognition tasks. The selected categories span the range of 0D objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces as well as patterned surfaces, and 3D structures such as microelectromechanical system (MEMS) devices and pillars. Additional categories such as tips and biological are also included to expand the spectrum of possible images. A preliminary degree of hierarchy is introduced, by creating a subtree structure for the categories and populating them with the available images, wherever possible.

Evolution of Reduced Graphene Oxide-SnS2 Hybrid Nanoparticle Electrodes in Li-Ion Batteries.

Hybrid nanomaterials where active battery nanoparticles are synthesized directly onto conductive additives such as graphene hold the promise of improving the cyclability and energy density of conversion and alloying type Li-ion battery electrodes. Here we investigate the evolution of hybrid reduced graphene oxide-tin sulfide (rGO-SnS2) electrodes during battery cycling. These hybrid nanoparticles are synthesized by a one-step solvothermal microwave reaction which allows for simultaneous synthesis of the SnS2 nanocrystals and reduction of GO. Despite the hybrid architecture of these electrodes, electrochemical impedance spectroscopy shows that the impedance doubles in about 25 cycles and subsequently gradually increases, which may be caused by an irreversible surface passivation of rGO by sulfur enriched conversion products. This surface passivation is further confirmed by post-mortem Raman spectroscopy of the electrodes, which no longer detects rGO peaks after 100 cycles. Moreover, galvanostatic intermittent titration analysis during the 1st and 100th cycles shows a drop in Li-ion diffusion coefficient of over an order of magnitude. Despite reports of excellent cycling performance of hybrid nanomaterials, our work indicates that in certain electrode systems, it is still critical to further address passivation and charge transport issues between the active phase and the conductive additive in order to retain high energy density and cycling performance.

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition.

In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

Structural Evolution of Electrochemically Lithiated MoS2 Nanosheets and the Role of Carbon Additive in Li-Ion Batteries.

Understanding the structure and phase changes associated with conversion-type materials is key to optimizing their electrochemical performance in Li-ion batteries. For example, molybdenum disulfide (MoS2) offers a capacity up to 3-fold higher (∼1 Ah/g) than the currently used graphite anodes, but they suffer from limited Coulombic efficiency and capacity fading. The lack of insights into the structural dynamics induced by electrochemical conversion of MoS2 still hampers its implementation in high energy-density batteries. Here, by combining ab initio density-functional theory (DFT) simulation with electrochemical analysis, we found new sulfur-enriched intermediates that progressively insulate MoS2 electrodes and cause instability from the first discharge cycle. Because of this, the choice of conductive additives is critical for the battery performance. We investigate the mechanistic role of carbon additive by comparing equal loading of standard Super P carbon powder and carbon nanotubes (CNTs). The latter offer a nearly 2-fold increase in capacity and a 45% reduction in resistance along with Coulombic efficiency of over 90%. These insights into the phase changes during MoS2 conversion reactions and stabilization methods provide new solutions for implementing cost-effective metal sulfide electrodes, including Li-S systems in high energy-density batteries.