Enhancing Laser-Induced Graphene via Integration of Gold Nanoparticles and Titanium Dioxide for Sensing and Robotics Applications.

Laser-induced graphene (LIG) is a promising material for various applications due to its unique properties and facile fabrication. However, the electrochemical performance of LIG is significantly lower than that of pure graphene, limiting its practical use. Theoretically, integrating other conductive materials with LIG can enhance its performance. In this study, we investigated the effects of incorporating gold nanoparticles (AuNPs) and titanium dioxide (TiO2) into LIG on its electrochemical properties using ReaxFF molecular dynamics (MD) simulations and experimental validation. We found that both AuNPs and TiO2 improved the work function and surface potential of LIG, resulting in a remarkable increase in output voltage by up to 970.5% and output power density by 630% compared to that of pristine LIG. We demonstrated the practical utility of these performance-enhanced LIG by developing motion monitoring devices, self-powered sensing systems, and robotic hand platforms. Our work provides new insights into the design and optimization of LIG-based devices for wearable electronics and smart robotics, contributing to the advancement of sustainable technologies.

Cathode Electrolyte Interphase Engineering for Prussian Blue Analogues in Lithium-Ion Batteries.

The increasing use of low-cost lithium iron phosphate cathodes in low-end electric vehicles has sparked interest in Prussian blue analogues (PBAs) for lithium-ion batteries. A major challenge with iron hexacyanoferrate (FeHCFe), particularly in lithium-ion systems, is its slow kinetics in organic electrolytes and valence state inactivation in aqueous ones. We have addressed these issues by developing a polymeric cathode electrolyte interphase (CEI) layer through a ring-opening reaction of ethylene carbonate triggered by OH- radicals from structural water. This facile approach considerably mitigates the sluggish electrochemical kinetics typically observed in organic electrolytes. As a result, FeHCFe has achieved a specific capacity of 125 mAh g-1 with a stable lifetime over 500 cycles, thanks to the effective activation of Fe low-spin states and the structural integrity of the CEI layers. These advancements shed light on the potential of PBAs to be viable, durable, and efficient cathode materials for commercial use.

Effects of bilateral tDCS over DLPFC on response inhibition, craving, and brain functional connectivity in Internet gaming disorder: A randomized, double-blind, sham-controlled trial with fMRI.

Background and aims: Impaired inhibitory control accompanied by enhanced craving is hallmark of addiction. This study investigated the effects of transcranial direct current stimulation (tDCS) on response inhibition and craving in Internet gaming disorder (IGD). We examined the brain changes after tDCS and their correlation with clinical variables. Methods: Twenty-four males with IGD were allocated randomly to an active or sham tDCS group, and data from 22 participants were included for analysis. Participants self-administered bilateral tDCS over the dorsolateral prefrontal cortex (DLPFC) for 10 sessions. Stop-signal tasks were conducted to measure response inhibition and participants were asked about their cravings for Internet gaming at baseline and post-tDCS. Functional magnetic resonance imaging data were collected at pre- and post-tDCS, and group differences in resting-state functional connectivity (rsFC) changes from the bilateral DLPFC and nucleus accumbens were examined. We explored the relationship between changes in the rsFC and behavioral variables in the active tDCS group. Results: A significant group-by-time interaction was observed in response inhibition. After tDCS, only the active group showed a decrease in the stop-signal reaction time (SSRT). Although craving decreased, there were no significant group-by-time interactions or group main effects. The anterior cingulate cortex (ACC) showed group differences in post- versus pre-tDCS rsFC from the right DLPFC. The rsFC between the ACC and left middle frontal gyrus was negatively correlated with the SSRT. Discussion and conclusion: Our study provides preliminary evidence that bilateral tDCS over the DLPFC improves inhibitory control and could serve as a therapeutic approach for IGD.

Impact of scleral tunnel length on the position of IOLs in flanged intrascleral haptic fixation.

PURPOSE: To investigate the effect of scleral tunnel length on the effective lens position and tilt of the intraocular lens (IOL) in flanged intrascleral haptic fixation (ISHF) using anterior segment optical coherence tomography (AS-OCT). SETTING: Tertiary institution. DESIGN: Retrospective case-control study. METHODS: This study included 55 and 42 eyes that underwent ISHF with 1.0- and 2.0-mm scleral tunnels, respectively. Twenty-three eyes that underwent sutured fixation were used as a control. The anterior chamber depth (ACD), scleral tunnel length, incident angle of haptic, and tilting of optic were analyzed using AS-OCT. RESULTS: The mean postoperative ACD, vertical tilt angle, and spherical equivalent of the 1.0-mm were 5.27 ± 0.39 mm, 6.04 ± 4.87°, and 0.38 ± 1.03 D, respectively. The ACD and vertical tilt angle of the 1.0-mm were larger than those of the others (p<0.001 and p<0.05, respectively), and the postoperative spherical equivalent was more hyperopic (p<0.05). The 2.0-mm exhibited a lower frequency of tilting greater than 7°. The inter-eye difference in ACD between in-the-bag fixation and ISHF of the1.0-mm tunnel was significantly greater than that in the 2.0-mm tunnel (p<0.05). The 1.0 mm tunnel had a significantly larger incident angle and a longer tunnel length (p<0.001, respectively) and showed a greater difference in the tunnel length on both sides (p<0.05). CONCLUSION: A shorter tunnel yielded a more unstable IOL position, greater variation in angle and tunnel length, and longer ACD during ISHF. An exact 2.0-mm tunnel must be created on both sides to achieve a stable and predictable IOL position.

Brain connectome correlates of short-term motor learning in healthy older subjects.

The motor learning process entails plastic changes in the brain, especially in brain network reconfigurations. In the current study, we sought to characterize motor learning by determining changes in the coupling behaviour between the brain functional and structural connectomes on a short timescale. 39 older subjects (age: mean (SD) = 69.7 (4.7) years, men:women = 15:24) were trained on a visually guided sequential hand grip learning task. The brain structural and functional connectomes were constructed from diffusion-weighted MRI and resting-state functional MRI, respectively. The association of motor learning ability with changes in network topology of the brain functional connectome and changes in the correspondence between the brain structural and functional connectomes were assessed. Motor learning ability was related to decreased efficiency and increased modularity in the visual, somatomotor, and frontoparietal networks of the brain functional connectome. Between the brain structural and functional connectomes, reduced correspondence in the visual, ventral attention, and frontoparietal networks as well as the whole-brain network was related to motor learning ability. In addition, structure-function correspondence in the dorsal attention, ventral attention, and frontoparietal networks before motor learning was predictive of motor learning ability. These findings indicate that, in the view of brain connectome changes, short-term motor learning is represented by a detachment of the brain functional from the brain structural connectome. The structure-function uncoupling accompanied by the enhanced segregation into modular structures over the core functional networks involved in the learning process may suggest that facilitation of functional flexibility is associated with successful motor learning.

CONTRIBUTION FACTORS OF EFFECTIVE LENS POSITION, TILT, AND DECENTRATION DURING FLANGED SCLERAL FIXATION OF INTRAOCULAR LENS: A Model Eye Study.

PURPOSE: The authors aimed to elucidate the factors related to effective lens position, tilt, and decentration of scleral fixed intraocular lenses (IOLs) with a flanged haptic technique in an artificial eye model using anterior segment optical coherence tomography. METHODS: Two bent 27-gauge needles were passed through a 1.0- or 2.0-mm scleral tunnel, 2.0 mm posterior to the limbus and 180° apart. Both haptics of a three-piece IOL were docked with guide needles and externalized. Factors related to the IOL position were analyzed using anterior segment optical coherence tomography and a stereomicroscope. RESULTS: The 1.0-mm scleral tunnel induced a significantly longer effective lens position than the 2.0-mm tunnel and suture fixation ( P < 0.05 and P < 0.01, respectively). Discrepancy in scleral tunnel length induced higher decentration of the optic to the opposite side of the haptic-embedded shorter tunnel and tilt perpendicular to the fixed axis than that in the scleral tunnel of the same length ( P < 0.001 and P < 0.05, respectively). If the scleral fixation points of both haptics are not exactly 180° apart, the IOL may become decentered and tilted ( P < 0.01 and P < 0.05, respectively). CONCLUSION: In the flanged haptic technique, the length, balance, and position of both scleral tunnels determine IOL effective lens position, tilt, and decentration.

Noninvasive theta-burst stimulation of the human striatum enhances striatal activity and motor skill learning.

The stimulation of deep brain structures has thus far only been possible with invasive methods. Transcranial electrical temporal interference stimulation (tTIS) is a novel, noninvasive technology that might overcome this limitation. The initial proof-of-concept was obtained through modeling, physics experiments and rodent models. Here we show successful noninvasive neuromodulation of the striatum via tTIS in humans using computational modeling, functional magnetic resonance imaging studies and behavioral evaluations. Theta-burst patterned striatal tTIS increased activity in the striatum and associated motor network. Furthermore, striatal tTIS enhanced motor performance, especially in healthy older participants as they have lower natural learning skills than younger subjects. These findings place tTIS as an exciting new method to target deep brain structures in humans noninvasively, thus enhancing our understanding of their functional role. Moreover, our results lay the groundwork for innovative, noninvasive treatment strategies for brain disorders in which deep striatal structures play key pathophysiological roles.

Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.

The identification of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) has been studied based on the subtle morphological changes in the brain. One of the typical approaches is a deep learning-based patch-level feature representation. For this approach, however, the predetermined patches before learning the diagnostic model can limit classification performance. To mitigate this problem, we propose the BrainBagNet with a position-based gate (PG), which applies position information of brain images represented through the 3D coordinates. Our proposed method represents the patch-level class evidence based on both MR scan and position information for image-level prediction. To validate the effectiveness of our proposed framework, we conducted comprehensive experiments comparing it with state-of-the-art methods, utilizing two publicly available datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle (AIBL) dataset. Furthermore, our experimental results demonstrate that our proposed method outperforms the existing competing methods in terms of classification performance for both AD diagnosis and mild cognitive impairment conversion prediction tasks. In addition, we performed various analyses of the results from diverse perspectives to obtain further insights into the underlying mechanisms and strengths of our proposed framework. Based on the results of our experiments, we demonstrate that our proposed framework has the potential to advance deep-learning-based patch-level feature representation studies for AD diagnosis and MCI conversion prediction. In addition, our method provides valuable insights, such as interpretability, and the ability to capture subtle changes, into the underlying pathological processes of AD and MCI, benefiting both researchers and clinicians.

Impact of glycemic control on the progression of aortic stenosis: a single-center cohort study using a common data model.

BACKGROUND: Diabetes mellitus (DM) is a well-established risk factor for the progression of degenerative aortic stenosis (AS). However, no study has investigated the impact of glycemic control on the rate of AS progression. We aimed to assess the association between the degree of glycemic control and the AS progression, using an electronic health record-based common data model (CDM). METHODS: We identified patients with mild AS (aortic valve [AV] maximal velocity [Vpeak] 2.0-3.0 m/sec) or moderate AS (Vpeak 3.0-4.0 m/sec) at baseline, and follow-up echocardiography performed at an interval of ≥ 6 months, using the CDM of a tertiary hospital database. Patients were divided into 3 groups: no DM (n = 1,027), well-controlled DM (mean glycated hemoglobin [HbA1c] < 7.0% during the study period; n = 193), and poorly controlled DM (mean HbA1c ≥ 7.0% during the study period; n = 144). The primary outcome was the AS progression rate, calculated as the annualized change in the Vpeak (△Vpeak/year). RESULTS: Among the total study population (n = 1,364), the median age was 74 (IQR 65-80) years, 47% were male, the median HbA1c was 6.1% (IQR 5.6-6.9), and the median Vpeak was 2.5 m/sec (IQR 2.2-2.9). During follow-up (median 18.4 months), 16.1% of the 1,031 patients with mild AS at baseline progressed to moderate AS, and 1.8% progressed to severe AS. Among the 333 patients with moderate AS, 36.3% progressed to severe AS. The mean HbA1c level during follow-up showed a positive relationship with the AS progression rate (ß = 2.620; 95% confidence interval [CI] 0.732-4.507; p = 0.007); a 1%-unit increase in HbA1c was associated with a 27% higher risk of accelerated AS progression defined as △Vpeak/year values > 0.2 m/sec/year (adjusted OR = 1.267 per 1%-unit increase in HbA1c; 95% CI 1.106-1.453; p < 0.001), and HbA1c ≥ 7.0% was significantly associated with an accelerated AS progression (adjusted odds ratio = 1.524; 95% CI 1.010-2.285; p = 0.043). This association between the degree of glycemic control and AS progression rate was observed regardless of the baseline AS severity. CONCLUSION: In patients with mild to moderate AS, the presence of DM, as well as the degree of glycemic control, is significantly associated with accelerated AS progression.

Boosting the interfacial superionic conduction of halide solid electrolytes for all-solid-state batteries.

Designing highly conductive and (electro)chemical stable inorganic solid electrolytes using cost-effective materials is crucial for developing all-solid-state batteries. Here, we report halide nanocomposite solid electrolytes (HNSEs) ZrO2(-ACl)-A2ZrCl6 (A = Li or Na) that demonstrate improved ionic conductivities at 30 °C, from 0.40 to 1.3 mS cm-1 and from 0.011 to 0.11 mS cm-1 for Li+ and Na+, respectively, compared to A2ZrCl6, and improved compatibility with sulfide solid electrolytes. The mechanochemical method employing Li2O for the HNSEs synthesis enables the formation of nanostructured networks that promote interfacial superionic conduction. Via density functional theory calculations combined with synchrotron X-ray and 6Li nuclear magnetic resonance measurements and analyses, we demonstrate that interfacial oxygen-substituted compounds are responsible for the boosted interfacial conduction mechanism. Compared to state-of-the-art Li2ZrCl6, the fluorinated ZrO2-2Li2ZrCl5F HNSE shows improved high-voltage stability and interfacial compatibility with Li6PS5Cl and layered lithium transition metal oxide-based positive electrodes without detrimentally affecting Li+ conductivity. We also report the assembly and testing of a Li-In||LiNi0.88Co0.11Mn0.01O2 all-solid-state lab-scale cell operating at 30 °C and 70 MPa and capable of delivering a specific discharge of 115 mAh g-1 after almost 2000 cycles at 400 mA g-1.

Design Principles for Fluorinated Interphase Evolution via Conversion-Type Alloying Processes for Anticorrosive Lithium Metal Anodes.

Over the past decade, lithium metal has been considered the most attractive anode material for high-energy-density batteries. However, its practical application has been hindered by its high reactivity with organic electrolytes and uncontrolled dendritic growth, resulting in poor Coulombic efficiency and cycle life. In this paper, we propose a design strategy for interface engineering using a conversion-type reaction of metal fluorides to evolve a LiF passivation layer and Li-M alloy. Particularly, we propose a LiF-modified Li-Mg-C electrode, which demonstrates stable long-term cycling for over 2000 h in common organic electrolytes with fluoroethylene carbonate (FEC) additives and over 700 h even without additives, suppressing unwanted side reactions and Li dendritic growth. With the help of phase diagrams, we found that solid-solution-based alloying not only facilitates the spontaneous evolution of a LiF layer and bulk alloy but also enables reversible Li plating/stripping inward to the bulk, compared with intermetallic compounds with finite Li solubility.

Characteristics of perivascular space dilatation in normal aging.

The increased incidence of dilated perivascular spaces (dPVSs) visible on MRI has been observed with advancing age, but the relevance of PVS dilatation to normal aging across the lifespan has yet to be fully clarified. In the current study, we sought to find out the age dependence of dPVSs by exploring changes in different characteristics of PVS dilatation across a wide range of age. For 1220 healthy subjects aged between 18 and 100 years, PVSs were automatically segmented and characteristics of PVS dilatation were assessed in terms of the burden, location, and morphology of PVSs in the white matter (WM) and basal ganglia (BG). A machine learning model using the random forests method was constructed to estimate the subjects' age by employing the PVS features. The constructed machine learning model was able to estimate the age of the subjects with an error of 9.53 years on average (correlation = 0.875). The importance of the PVS features indicated the primary contribution of the burden of PVSs in the BG and the additional contribution of locational and morphological changes of PVSs, specifically peripheral extension and reduced linearity, in the WM to age estimation. Indeed, adding the PVS location or morphology features to the PVS burden features provided an improvement to the performance of age estimation. The age dependence of dPVSs in terms of such various characteristics of PVS dilatation in healthy subjects could provide a more comprehensive reference for detecting brain disease-related PVS dilatation.

Early motor skill acquisition in healthy older adults: brain correlates of the learning process.

Motor skill learning is a crucial process at all ages. However, healthy aging is often accompanied by a reduction in motor learning capabilities. This study characterized the brain dynamics of healthy older adults during motor skill acquisition and identified brain regions associated with changes in different components of performance. Forty-three subjects participated in a functional magnetic resonance imaging study during which they learned a sequential grip force modulation task. We evaluated the continuous changes in brain activation during practice as well as the continuous performance-related changes in brain activation. Practice of the motor skill was accompanied by increased activation in secondary motor and associative areas. In contrast, visual and frontal areas were less recruited as task execution progressed. Subjects showed significant improvements on the motor skill. While faster execution relied on parietal areas and was inversely associated with frontal activation, accuracy was related to activation in primary and secondary motor areas. Better performance was achieved by the contribution of parietal regions responsible for efficient visuomotor processing and cortical motor regions involved in the correct action selection. The results add to the understanding of online motor learning in healthy older adults, showing complementary roles of specific networks for implementing changes in precision and speed.

Stratified predictions of upper limb motor outcomes after stroke.

Introduction: Longitudinal observations of upper limb motor recovery after stroke have suggested that certain subgroups may exhibit distinct recovery patterns. Here we sought to examine whether the predictive ability for post-stroke upper limb motor outcomes could be enhanced by applying conventional stratification strategies. Method: For 60 individuals who suffered the first stroke, upper limb motor impairment was assessed with the upper extremity Fugl-Meyer assessment (UE-FMA) at 2 weeks as a baseline and then 3 months post-stroke. Brain structural damage at baseline was assessed by MRI data-derived markers ranging from traditional lesion size to the lesion load and to the disconnectome. Linear regression models for predicting upper limb motor outcomes (UE-FMA score at 3 months post-stroke) based on baseline upper limb motor impairment (UE-FMA score at 2 weeks post-stroke), brain structural damage, and their combinations were generated, and those with the best predictive performance were determined for individual subgroups stratified according to initial impairment (severe and non-severe), lesion location (cortical and non-cortical), and neurophysiological status (motor evoked potential-positive and motor evoked potential-negative). Results: The best predictions were made by baseline upper limb motor impairment alone for subgroups with less functional impairment (non-severe) or less structural involvement (non-cortical), but by the combination of baseline upper limb motor impairment and brain structural damage for the other subgroups. The predictive models tailored for subgroups determined according to initial impairment and neurophysiological status yielded a smaller overall error than that for the whole group in upper limb motor outcome predictions. Discussion: The predictive ability for upper limb motor outcomes could be enhanced beyond the one-size-fits-all model for all individuals with stroke by applying specific stratification strategies, with stratification according to initial impairment being the most promising. We expect that predictive models tailored for individual subgroups could lead closer to the personalized prognosis of upper limb motor outcomes after stroke.

Using Single-Crystal Graphene to Form Arrays of Nanocapsules Enabling the Observation of Light Elements in Liquid Cell Transmission Electron Microscopy.

We have designed and fabricated a TEM (transmission electron microscopy) liquid cell with hundreds of graphene nanocapsules arranged in a stack of two Si3N4-x membranes. These graphene nanocapsules are formed on arrays of nanoholes patterned on the Si3N4-x membrane by focused ion beam milling, allowing for better resolution than for the conventional graphene liquid cells, which enables the observation of light elements, such as atomic structures of silicon. We suggest that multiple nanocapsules provide opportunities for consecutive imaging under the same conditions in a single liquid cell. The use of single-crystal graphene windows offers an excellent signal-to-noise ratio and high spatial resolution. The motion of silicon nanoparticles (a low atomic number (Z) material) interacting with nanobubbles was observed, and analyzed, in detail. Our approach will help advance liquid-phase TEM observations by providing a straightforward method to encapsulate liquid between monolayers of various 2-dimensional materials.

Scrambler Therapy for Chronic Pain after Burns and Its Effect on the Cerebral Pain Network: A Prospective, Double-Blinded, Randomized Controlled Trial.

Chronic pain is common after burn injuries, and post-burn neuropathic pain is the most important complication that is difficult to treat. Scrambler therapy (ST) is a non-invasive modality that uses patient-specific electrocutaneous nerve stimulation and is an effective treatment for many chronic pain disorders. This study used magnetic resonance imaging (MRI) to evaluate the pain network-related mechanisms that underlie the clinical effect of ST in patients with chronic burn-related pain. This prospective, double-blinded, randomized controlled trial (ClinicalTrials.gov: NCT03865693) enrolled 43 patients who were experiencing chronic neuropathic pain after unilateral burn injuries. The patients had moderate or greater chronic pain (a visual analogue scale (VAS) score of ≥5), despite treatment using gabapentin and other physical modalities, and were randomized 1:1 to receive real or sham ST sessions. The ST was performed using the MC5-A Calmare device for ten 45 min sessions (Monday to Friday for 2 weeks). Baseline and post-treatment parameters were evaluated subjectively using the VAS score for pain and the Hamilton Depression Rating Scale; MRI was performed to identify objective central nervous system changes by measuring the cerebral blood volume (CBV). After 10 ST sessions (two weeks), the treatment group exhibited a significant reduction in pain relative to the sham group. Furthermore, relative to the pre-ST findings, the post-ST MRI evaluations revealed significantly decreased CBV in the orbito-frontal gyrus, middle frontal gyrus, superior frontal gyrus, and gyrus rectus. In addition, the CBV was increased in the precentral gyrus and postcentral gyrus of the hemisphere associated with the burned limb in the ST group, as compared with the CBV of the sham group. Thus, a clinical effect from ST on burn pain was observed after 2 weeks, and a potential mechanism for the treatment effect was identified. These findings suggest that ST may be an alternative strategy for managing chronic pain in burn patients.

Evaluating reproducibility and subject-specificity of microstructure-informed connectivity.

Tractography enables identifying and evaluating the healthy and diseased brain's white matter pathways from diffusion-weighted magnetic resonance imaging data. As previous evaluation studies have reported significant false-positive estimation biases, recent microstructure-informed tractography algorithms have been introduced to improve the trade-off between specificity and sensitivity. However, a major limitation for characterizing the performance of these techniques is the lack of ground truth brain data. In this study, we compared the performance of two relevant microstructure-informed tractography methods, SIFT2 and COMMIT, by assessing the subject specificity and reproducibility of their derived white matter pathways. Specifically, twenty healthy young subjects were scanned at eight different time points at two different sites. Subject specificity and reproducibility were evaluated using the whole-brain connectomes and a subset of 29 white matter bundles. Our results indicate that although the raw tractograms are more vulnerable to the presence of false-positive connections, they are highly reproducible, suggesting that the estimation bias is subject-specific. This high reproducibility was preserved when microstructure-informed tractography algorithms were used to filter the raw tractograms. Moreover, the resulting track-density images depicted a more uniform coverage of streamlines throughout the white matter, suggesting that these techniques could increase the biological meaning of the estimated fascicles. Notably, we observed an increased subject specificity by employing connectivity pre-processing techniques to reduce the underlaying noise and the data dimensionality (using principal component analysis), highlighting the importance of these tools for future studies. Finally, no strong bias from the scanner site or time between measurements was found. The largest intraindividual variance originated from the sole repetition of data measurements (inter-run).

Simulating the progression of brain structural alterations in Parkinson's disease.

Considering brain structural alterations as neurodegenerative consequences of Parkinson's disease (PD), we sought to infer the progression of PD via the ordering of brain structural alterations from cross-sectional MRI observations. Having measured cortical thinning in gray matter (GM) regions and disintegrity in white matter (WM) regions as MRI markers of structural alterations for 130 patients with PD (69 ± 10 years, 72 men), stochastic simulation based on the probabilistic relationship between the brain regions was conducted to infer the ordering of structural alterations across all brain regions and the staging of structural alterations according to changes in clinical status. The ordering of structural alterations represented WM disintegrity tending to occur earlier than cortical thinning. The staging of structural alterations indicated structural alterations happening mostly before major disease complications such as postural instability and dementia. Later disease states predicted by the sequence of structural alterations were significantly related to more severe clinical symptoms. The relevance of the ordering of brain structural alterations to the severity of clinical symptoms suggests the clinical feasibility of predicting PD progression states.

Nitrogen Plasma-Assisted Functionalization of Silicon/Graphite Anodes to Enable Fast Kinetics.

The practical use of silicon anodes is interfered by the following key factors: volume expansion, slow kinetics, and low electrical and ionic conductivities. Many studies have focused on surface engineering from the particle to electrode level to achieve stability and energy density. Herein, simple nitrogen gas plasma is introduced as a surface treatment method for silicon-based electrodes to avoid the problems of material synthesis-based functionalizations (e.g., high cost, time consuming, and low quality). The introduction of activated nitrogen gas on electrode surfaces changes the binding energy and resistance of silicon, increasing the reversibility of the charge/discharge reaction of silicon-based anodes. In addition, such doping and dehydrogenation of the electrode surface improve reaction kinetics to 876 mA h g-1 specific capacity at 8.5 A g-1 in silicon/graphite anodes even with a high silicon content of 40%. The proposed strategy, through nitrogen plasma, offers advantages for direct functionalization on electrode surfaces by a simple method.

Predicting superagers by machine learning classification based on the functional brain connectome using resting-state functional magnetic resonance imaging.

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.