Volume Changes Research Articles

Morphological Evolution of Sn-Metal-Based Anodes for Lithium-Ion Batteries Using Operando X-Ray Imaging.

Sn-based electrodes are promising candidates for next-generation lithium-ion batteries. However, it suffers from deleterious micro-structural deformation as it undergoes drastic volume changes upon lithium insertion and extraction. Progress in designing these materials is limited to complex structures. There is a significant need to develop an alloy-based anode that can be industrially manufactured and offers high reversible capacity. This necessitates a profound understanding of the interplay between structural changes and electrochemical performance. Here, operando X-ray imaging is used to correlate the morphological evolution to electrochemical performance in foil and foam systems. The 3D Sn-foam-like structure electrode is fabricated in-house as a practical approach to accommodate the volume expansion and alleviate the mechanical stress experienced upon alloying/dealloying. Results show that generating pores in Sn electrodes can help manage the volume expansion and mitigate the severe mechanical stress in thick electrodes during alloying/dealloying processes. The foam electrode demonstrates superior electrochemical performance compared to non-porous Sn foil with an equivalent absolute capacity. This work advances the understanding of the real-time morphological evolution of Sn bulky electrodes.

Pressure Protein Denaturation Compared to Thermal and Chemical Unfolding: Analyses with Cooperative Models.

The thermodynamics of pressure-induced protein denaturation could so far not be directly compared with protein denaturation induced by temperature or chemical agents. Here, we provide a new cooperative model for pressure-induced protein denaturation that allows the quantitative comparison of all three denaturing processes based on their free energy, enthalpy, entropy, and cooperativity. As model proteins, we use apolipoprotein A-1 and lysozyme. The comparison shows that heat-induced unfolding is the most cooperative process. It is characterized by large positive enthalpies and entropies and (due to enthalpy-entropy compensation) small negative free energies. Pressure denaturation is less cooperative. The entropies and enthalpies are less positive, and the resulting free energies are more negative. Chemically induced unfolding is the least cooperative and shows the most negative free energies, in particular, if guanidinium hydrochloride (exhibiting a high binding affinity to certain proteins) is used as a denaturant. The three unfolding processes differ not only with respect to their cooperativity and the thermodynamic parameters but also with respect to the volume changes, suggesting structural differences of the denatured proteins. Using cooperative models thus yields significant new insights into the protein unfolding/folding processes.

Utilizing Patient-Derived Xenografts to Model Precision Oncology for Biliary Tract Cancer.

Biliary tract cancers, which are rare and aggressive malignancies, are rich in clinically actionable molecular alterations. A major challenge in the field is the paucity of clinically relevant biliary tract cancer models that recapitulate the diverse molecular profiles of these tumors. The purpose of this study was to curate a collection of patient-derived xenograft (PDX) models that reflect the spectrum of genomic alterations present in biliary tract cancers to create a resource for modeling precision oncology. PDXs were derived from biliary tract cancer samples collected from surgical resections or metastatic biopsies. Alterations present in the PDXs were identified by whole-exome sequencing and RNA sequencing. PDXs were treated with approved and investigational agents. Efficacy was assessed by change in tumor volume from baseline. Event-free survival was defined as the time to tumor doubling from baseline. Responses were categorized at day 21: >30% decrease in tumor volume = partial response, >20% increase in tumor volume = progressive disease, and any non-partial response/progressive disease was considered stable disease. Genomic sequencing demonstrated key actionable alterations across this cohort, including alterations in FGFR2, isocitrate dehydrogenase I, ERRB2, PIK3CA, PTEN, and KRAS. RNA sequencing demonstrated fusions and expression of antibody-drug conjugate targets, including TROP2, HER2, and Nectin4. Therapeutic matching revealed objective responses to approved and investigational agents that have been shown to have antitumor activity clinically. In this study, we developed a catalog of biliary tract cancer PDXs that underwent comprehensive molecular profiling and therapeutic modeling. To date, this is one of the largest collections of biliary tract cancer PDX models and will facilitate the development of personalized treatments for patients with these aggressive malignancies.

Dynamic Expansion and Contraction of Multiple Sclerosis T2-Weighted Hyperintense Lesions Are Present Below the Threshold of Visual Perception.

The study of T2-weighted hyperintense lesions resulting from autoimmune inflammatory injury and associated volumes within the CNS remains fundamental to the diagnosis and disease surveillance of MS. We investigated the dynamic changes of individual T2-weighted hyperintense MS lesions on MRI and hypothesized that variations may be present below the threshold of visual perception when evaluating longitudinal data. A retrospective study was performed of people with MS, incorporating data from 3 consecutive MRI time points acquired within a single academic center. All included MRI studies lacked formal imaging interpretations of newly enlarging or contracting T2-weighted hyperintensities. Well-defined, noncoalescing, individual T2-weighted hyperintense lesions were targeted. A total of 8-12 lesions were randomly selected in a blinded fashion at MRI time point 1 and 3D lesion volumes were followed over MRI time points 2 and 3. The impact of treatment on lesion expansion and relationship to brain MRI advancement, patient-reported progression of disease, and physician-identified progression was also studied. The study cohort comprised 115 people (81 (70.4%) women; mean disease duration of 9.36 years [standard deviation: 7.72 years]) who were primarily White (79.1%). A total of 1426 focal T2-weighted hyperintense MS lesions were identified on MRI time point 1 and longitudinally followed over MRI time points 2 and 3. In the evaluation of raw changes in individual T2-weighted hyperintense lesion volumes from MRI time point 1 to MRI time point 2, a similar number of individuals were observed with predominantly expanding (49/115; 42.6%) or contracting (51/115; 44.3%) lesions. However, most lesions expanded in volume (48/115; 41.7%) versus those that contracted (45/115; 39.1%) when evaluating MRI time point 3 to time point 1. Those individuals not on active treatment had a 67.15% reduction in the odds of more individual lesions predominantly contracting in volume relative to those on low-efficacy disease modifying therapy treatment (95% CI = [-83.89% to -33.01%], P = .0008) and 74.02% reduction relative to high-efficacy treatment individuals (95% CI = [-87.37% to -46.56%], P < .0001). Dynamic changes in T2-weighted hyperintense lesions are abundant, occurring below the threshold of visual perception and are present more frequently in untreated individuals.

Pre-Lithiated Silicon-Based Composite Anode for High-Performance All-Solid-State Batteries.

Silicon is widely recognized as a promising anode material for all-solid-state batteries (ASSBs) due to exceptional specific capacity, abundant availability, and environmental sustainability. However, the considerable volume expansion and particle fragmentation of Si during cycling lead to significant performance degradation, limiting its practical application. Herein, the development of a pre-lithiated Si-based composite anode (c-Li1Si) is presented, designed to address the key challenges faced by Si-based anodes, namely severe volume changes and low electrochemical stability. The c-Li1Si anodes are prepared by incorporating Li₁Si powders with Li6PS5Cl (LPSCl) sulfide solid electrolyte (SSE), forming a dense structure that enhances conductivity and mitigates structural degradation. The ASSBs with c-Li1Si-60 anode exhibit outstanding electrochemical performance, including excellent rate capability and capacity retention of 84.4% after 1000 cycles at 1C and exceptional performance even at low anode-to-cathode capacity ratios (N/P ratio) of 1.68. EIS and pressure measurements reveal improved reaction kinetics and reduced volume expansion. X-ray micro-CT and SEM further confirmed the introduction of LPSCl effectively alleviated volume changes and maintained electrode structural integrity, contributing to enhanced electrochemical performance. These results underscore the potential of the c-Li1Si anode to overcome the intrinsic limitations of Si-based anodes, offering a promising pathway toward high-energy-density ASSBs.

Utility of Diaphragm Dome Height as a Marker of Operational Lung Volume Changes, Disease Burden and Exacerbations in Patients with Mild-to-Moderate COPD: An Observational Study Within the CanCOLD Cohort

BackgroundDynamic hyperinflation is central to dyspnoea, exercise limitation, and exacerbations in chronic obstructive pulmonary disease (COPD). While studied previously in moderate-severe COPD, the relevance of diaphragm dome height on clinically important outcomes has been under-investigated in mild-to-moderate COPD.MethodsCanadian Cohort Obstructive Lung Disease participants with spirometry-confirmed COPD, symptom-limited incremental cardiopulmonary exercise testing, and computed tomography image data were included. Base-to-apex left and right diaphragm dome height (DDH) was automatically segmented, with increased height implying less flattening and thus less hyperinflation. Dynamic hyperinflation was defined as ≥150 mL reduction in inspiratory capacity (IC) from rest to peak-exercise. Cross-sectional linear regression models were fitted between left and right DDH (predictor variables) with peak IC (ICpeak), peak workload (Wpeak), FEV1, and CAT score (outcome variables), and in longitudinal (Anderson-Gill) models with “symptom-based” and “event-based” exacerbations. Results are reported as parameter estimates or hazard ratios with 95%CIs per interquartile range dome height increment.ResultsAmong 304 participants (mean age 64.7±10.3, 41.8% female, 44.4% with mild COPD), each left and right DDH increment, respectively, was associated with ICpeak(0.21[0.13,0.29]L, 0.13[0.07,0.19]L), Wpeak(9.54[5.03,14.04]W, 6.04[2.45,9.62]W), FEV1(0.17[0.10,0.25]L, 0.08[0.02,0.14]L), and CAT score (−1.36[−2.39,−0.33], −0.82[−1.63,0.00]). Left DDH alone was associated with both symptom-based (HR: 0.82[0.74,0.91]) and event-based (0.83[0.73,0.95]) exacerbations. Of 167/304 participants with confirmed dynamic hyperinflation (ΔIC=−0.47±0.25 L), left DDH alone was associated with all outcomes (ICpeak, Wpeak, FEV1, CAT, and symptom-based/event-based exacerbations).ConclusionsLeft DDH appears to be a clinically important marker for operational lung volume changes, lung function, exercise performance, disease burden, and exacerbations in mild-to-moderate COPD.

Changes in household purchasing of soft drinks following the UK soft drinks industry levy by household income and composition: controlled interrupted time series analysis, March 2014 to November 2019

BackgroundThe WHO recommends taxes on sugar sweetened beverages (SSBs) to improve population health. We examined changes in volume of and amount of sugar in purchases of soft drinks according to...

Effectiveness and safety of hypofractionated gamma knife radiosurgery for large meningiomas and those adjacent to the optic pathway and brainstem: preliminary therapeutic outcomes.

Recent technologic advancements have facilitated the use of hypofractionated Gamma Knife-based radiosurgery (HF-GKRS) to treat large lesions or those in eloquent areas. This study aimed to analyze the preliminary results of HF-GKRS for these meningiomas, and to determine its effectiveness and safety.This single-center retrospective study analyzed data of patients who underwent HF-GKRS for large meningiomas or those in eloquent areas with > 6 months of follow-up. The primary outcome was progression-free survival (PFS). The secondary outcomes were neurological deterioration, post-treatment T2 signal changes following HF-GKRS, and tumor volume changes. Volumetric analysis of the tumors after treatment was also performed to assess changes in tumor size after HF-GKRS.Overall, 24 patients with a median follow-up period of 22 months (range: 6-49 months) were included. Among them, 18 (75%) patients had tumors in close proximity to the optic pathway, and 15 (63%) patients had large lesions (> 10 cm3). The cumulative 1- and 3-year PFS rates were 100% and 92%, respectively. The cumulative 3-year rate of adverse radiation effects was 9%. Overall, 12 patients (50%) showed tumor reduction, with a median tumor reduction rate of 45% (range: 25-58%).Our preliminary results revealed that HF-GKRS for large meningiomas or those in eloquent areas is safe and effective, with satisfactory short- and mid-term PFS and low adverse radiation effects. Further research with more patients and longer follow-up periods is required.

Accuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023. Chest CTs were obtained just before and after thoracentesis. The volume of pleural fluid on each CT scan, with the difference representing the drained fluid volume, was measured by automated segmentation (fully-automated measurement). An expert thoracic radiologist then manually corrected these automated volume measurements (human-assisted measurement). Both fully-automated (median percentage error, 13.1%; maximum estimated 95% error range, 708 mL) and human-assisted measurements (median percentage error, 10.9%; maximum estimated 95% error range, 312 mL) systematically underestimated the volume of drained fluid, beyond the equivalence margin. The magnitude of underestimation increased proportionally to the drainage volume. Agreement between fully-automated and human-assisted measurements (intraclass correlation coefficient [ICC], 0.99), and the test-retest reliability of fully-automated (ICC, 0.995) and human-assisted (ICC, 0.997) measurements were excellent. These results highlight a potential systematic discrepancy between AI segmentation- based CT quantification of pleural effusion volume change and actual volume change. ©RSNA, 2025.

S, Se-Codoped Dual Carbon Coating and Se Substitution in Co-Alkoxide-Derived CoS2 Through SeS2 Triggered Selenization for High-Performance Sodium-Ion Batteries

The development of metal sulfides as anodes for sodium-ion batteries (SIBs) is significantly obstructed by the slow kinetics of the electrochemical reactions and the substantial volume changes on the cycling. Herein, we introduce a selenium-substituted cobalt disulfide embedded within a dual carbon–graphene framework (Se-CoS2/C@rGO) for high-performance SIBs. The Se-CoS2/C@rGO was prepared via a synchronous sulfurization/selenization strategy using Co-alkoxide as the precursor and SeS2 as the source of selenium and sulfur, during which the EG anions are converted in situ to a S, Se codoped carbon scaffold. The dual carbon–graphene matrix not only improves the electronic conductivity but also stabilizes the electrode material effectively. In addition, the Se substitution within the CoS2 lattice further improves the electrical conductivity and promotes the Na+ reaction kinetics. The enhanced intrinsic electronic/ionic conductivity and reinforced structural stability endow the Se-CoS2/C@rGO anode with a high reversible capacity (558.2 mAh g−1 at 0.2 A g−1), superior rate performance (351 mAh g−1 at 20 A g−1), and long cycle life (93.5% capacity retention after 2100 cycles at 1 A g−1). This work provides new insights into the development of stable and reversible anode materials through Se substitution and dual carbon encapsulation.

Eliminating osmotic stress during cryoprotectant loading: A mathematical investigation of solute-solvent transport.

Osmotic stresses during cryoprotectant loading induce changes in cellular volume, leading to membrane damage or even cell death. Appropriate model-guided mitigation of these osmotic gradients during cryoprotectant loading is currently lacking, but would be highly beneficial in reducing viability loss during the loading process. To address this need, we reformulate the two-parameter formalism described by Jacobs and Stewart for cryoprotectant loading under the constraint of constant cell volume. We then derive simple, concise, analytic solutions to these equations, showing the transient extracellular permeating and nonpermeating cryoprotectant concentrations required to load a cell at constant volume, thus eliminating osmotic stresses during cryoprotectant loading. Additionally, we show analytic approximations of both ramp (linear) as well as step-wise loading and how one can use the hydraulic conductivity Lp, membrane permeability Ps, cell volume Vo, and osmotically inactive fraction to derive cryoprotectant loading protocols that minimize osmotic stress. We also present timescales for water and cryoprotectant transport which can be used to estimate loading times as well as Lp and Ps. We discuss how previous optimized loading strategies are inherently sensitive to parameter uncertainties and biological variability, increasing the likelihood of exceeding critical osmotic limits. By contrast, the proposed protocol provides a larger buffer against deviations, offering a safer and more robust solution to CPA loading. Importantly, we demonstrate that the volume-loss-free CPA loading protocols outlined in this paper occur on the same timescale as conventional and step-loading methods, suggesting that these protocols could be a safer, more efficient alternative for CPA loading.

Design and Preparation of Soluble Polyimide Binder With Random Copolymerized Rigid‐Flexible Segments for Lithium Ion Batteries

ABSTRACTBinders play a critical role in the preparation and operation of electrodes in lithium‐ion batteries (LIBs). Developing new binding materials with high affinity to Li ions and resistance to higher voltages was desirable for the practical applications of LIBs. In this work, soluble polyimide binder (PI) with random copolymerized rigid‐flexible segments were synthesized and evaluated as cathode binder for the typical lithium iron phosphate cathode material in terms of cycling performances, rate capability, and electrochemical impedance. By introducing the polar and soft ether segment into the rigid PI chains, the NMP‐soluble PI precursors were obtained. The rigid‐flexible segments in the PI binders work synergistically during the electrochemical process, in which the rigid segments of aromatic units with strong lithiophilic CO group provided the necessary mechanical support and affinity with electrolytes, while the flexible segments endow the high solubility and buffer effect to the volume change during the charge/discharge process. Moreover, the 3,5‐diaminobenzoic acid (DABA) working as the crosslinking unit was introduced during the synthesis and the addition ratios were optimized. The as‐optimized PI binders exhibited superior rate performances and comparable cycling stability when compared with the commercial PVDF binder, demonstrating the great potential for PI binder in the LIBs systems.

A Phase-Transition-Free Sodium Vanadium Phosphate Cathode via Medium-Entropy Engineering for Superior Sodium Ion Batteries.

Na3V2(PO4)3, based on multi-electron reactions between V3+/V4+/V5+, is a promising cathode material for SIBs. However, its practical application is hampered by the inferior conductivity, large barrier of V4+/V5+, and stepwise phase transition. Herein, these issues are addressed by constructing a medium-entropy material (Na3.2V1.1Ti0.2Al0.2Cr0.2Mn0.2Ni0.1(PO4)3, ME-NVP) with strong ME─O bond and highly occupied Na2 sites. Benefiting from the medium-entropy effect, ME-NVP manifests a phase-transition-free reaction mechanism, two reversible plateaus at 3.4 (V3+/V4+) and 4.0V (V4+/V5+), and small volume change (2%) during Na+ insertion/extraction processes, as confirmed by comprehensive in/ex situ characterizations. Moreover, kinetics analysis illuminates the superior Na+ diffusion ability of ME-NVP. Thus, the ME-NVP cathode realizes remarkable rate capability of 67mA h g-1 at 50C and a long-term lifespan over 10000 cycles (capacity retention of 81.3%). Theoretical calculations further illustrate that the weak binding of Na+ ion in the channel is responsible for the rapid Na+ diffusion, accounting for the superior reaction kinetics. Moreover, rigid MEO6 octahedral and feasible rearrangement of Na+ ions can suppress the phase transition, thus endowing an ultrastable ME-NVP cathode. This work highlights the significant role of medium-entropy engineering in advancing the output voltage, cycling stability, and rate capability of polyanionic cathodes.

Comprehensive Monitoring of Construction Spoil Disposal Areas in High-Speed Railways Utilizing Integrated 3S Techniques

High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework’s capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects.

Association of CSF soluble TREM1 levels with hippocampal atrophy in cognitively impaired older adults

BackgroundRecent studies have shown that cerebrospinal fluid (CSF) levels of soluble triggering receptor expressed on myeloid cells 1 (sTREM1) are elevated in individuals with Alzheimer’s disease (AD), though the relationship between CSF sTREM1 and hippocampal atrophy remains to be elucidated. The primary aim of this study was to investigate the association between CSF sTREM1 levels and longitudinal changes in hippocampal volumes, and to determine if this relationship is moderated by cognitive status.MethodsWe included 576 participants, comprising 152 cognitively unimpaired (CU) and 424 cognitively impaired (CI) individuals. In the cross-sectional analyses, Pearson’s correlation tests were conducted to examine the relationship between baseline CSF sTREM1 levels and hippocampal volumes in both CU and CI participants. For the longitudinal analyses, a linear mixed-effects model was employed to assess the significance of the three-way interaction between CSF sTREM1 levels, cognitive status, and follow-up time on adjusted hippocampal volume (aHV). Further stratified analyses based on cognitive status were performed to dissect the specific effects within each group.ResultsOur findings revealed significantly elevated baseline CSF sTREM1 levels in CI participants compared to CU participants. Cross-sectional analyses demonstrated that CSF sTREM1 levels were negatively associated with hippocampal volumes in both CU and CI participants. In the longitudinal analyses, the three-way interaction between CSF sTREM1 levels, cognitive status, and follow-up time was found to be significant for aHV. Stratified analyses indicated that, in CI participants, higher CSF sTREM1 levels were associated with a more accelerated rate of hippocampal atrophy, whereas no such association was observed in CU participants.ConclusionThese results underscore the complex interplay between neuroinflammation, as reflected by CSF sTREM1 levels, hippocampal atrophy, and cognitive decline. The data suggest that neuroinflammation may contribute differently to hippocampal atrophy rates in CI versus CU individuals, highlighting the potential for targeted anti-inflammatory interventions in the prevention and treatment of AD.

The study of barocaloric effect in ferroelastic Pb3(VO4)2

Abstract The barocaloric effect is considered as one of the most promising refrigeration with the potential to replace traditional gas compression refrigeration. One of the main obstacles to the application of barocaloric materials lies in the requirement for high driving pressures. In this paper, we report on the barocaloric effect of Pb3(VO4)2, which exhibits a ferroelastic transition from a high-temperature trigonal structure to a low-temperature monoclinic structure at 357 K, accompanied by a substantial volume change. The entropy change induced by hydrostatic pressure can reach up 14 J·kg-1·K-1 under a relatively low pressure of 80 MPa. This work is expected to expand the selection range of barocaloric materials.

Association of ventilation volumes, pressures and rates with the mechanical power of ventilation in patients without acute respiratory distress syndrome: exploring the impact of rate reduction

SummaryIntroductionHigh mechanical power is associated with mortality in patients who are critically ill and require invasive ventilation. It remains uncertain which components of mechanical power – volume, pressure or rate – increase mechanical power the most.MethodsWe conducted a post hoc analysis of a database containing individual patient data from three randomised clinical trials of ventilation in patients without acute respiratory distress syndrome. The primary endpoint was mechanical power. We used linear regression; double stratification to create subgroups of participants; and mediation analysis to assess the impact of changes in volumes, pressures and rates on mechanical power.ResultsA total of 1732 patients were included and analysed. The median (IQR [range]) mechanical power was 12.3 (9.3–17.1 [3.7–50.1]) J.min‐1. In linear regression, respiratory rate (36%) and peak pressure (51%) explained most of the increase in mechanical power. Increasing quintiles of peak pressure stratified on constant levels of respiratory rate resulted in higher risks of high mechanical power (relative risk 2.2 (95%CI 1.8–2.6), p &lt; 0.01), while decreasing quintiles of respiratory rate stratified on constant levels of peak pressure resulted in lower risks of high mechanical power (relative risk 0.2 (95%CI 0.2–0.3), p &lt; 0.01). Mediation analysis showed that a reduction in respiratory rate, with the increase in tidal volume, partially mediates an effect of reduction in mechanical power (average causal mediation effect ‐0.10, 95%CI ‐0.12 to ‐0.09, p &lt; 0.01), but still with a direct effect of tidal volume on mechanical power (average direct effect 0.15, 95%CI 0.11–0.19, p &lt; 0.01).DiscussionIn this cohort of patients without acute respiratory distress syndrome, pressure and respiratory rate were the most important determinants of mechanical power. The respiratory rate may be the most attractive ventilator setting to adjust when targeting a lower mechanical power.

Effects of structural design, interface optimization, and processing on the performance of lithium battery anode silicon nanomaterials

With an emphasis on the advancements in anode material research, this article offers a comprehensive summary of the major technologies used in lithium-ion batteries, a common type of electrochemical energy storage device. Because silicon has both benefits, such as low cost and high theoretical specific capacity, and significant drawbacks, including electrode pulverization and capacity loss due to volume expansion, it is the main focus of this study. This article goes into great detail on the use of silicon material nanosizing to enhance silicon anode performance, discussing how reducing particle size can mitigate the negative effects of volume changes during cycling. However, the section on compositing and structural design processes lacks sufficient depth, which limits the understanding of how these processes can further improve anode stability and efficiency. Furthermore, the article does not comprehensively cover the diverse methods utilized to enhance silicon anode performance, such as the incorporation of conductive additives or protective coatings. The challenges associated with the practical applications of silicon nanoparticles, such as scalability and cost-effectiveness, are examined in detail; nevertheless, the prospects for further research and technological advancements in these areas are not sufficiently addressed. This paper calls for more in-depth studies and innovations in compositing techniques, structural optimization, and practical solutions to realize the full potential of silicon anodes for advanced energy storage.

High-strength elastomer separator for high-current-density-charging lithium metal batteries

Utilizing lithium metal as an anode in batteries has been expected to replace conventional lithium-ion batteries. However, the mechanical properties and electrochemical performance of current separators do not meet the requirements for practical applications of lithium metal batteries (LMBs). Here, we report an elastomer separator with an interconnected structure of plastic-crystal-embedded and garnet-conductor-regulated thermoplastic polystyrene-b-poly(ethylene-r-butylene)-b-polystyrene elastomer integrated with the polyethylene matrix. The 14-micron-thick elastomer separators show a combination of excellent elongation of ∼115.2% and sufficiently high tensile strength of ∼56 MPa. The elastomer separators accommodate volume changes and block dendrites for high-current-density cycling of LMBs. As a demonstration, the elastomer separators enable stable operation of LMBs under stringent conditions, a practical high loading of 18 mg cm−2 LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode at an extremely high charging/discharging current density of 1.8 mA cm−2, delivering a high reversible capacity of 164 mAh g−1 and capacity retention of 88% after 140 cycles.

A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction

As lithium-ion batteries become increasingly popular worldwide, accurately determining their capacity is crucial for various devices that rely on them. Numerous data-driven methods have been applied to evaluate battery-related parameters. In the application of these methods, input features play a critical role. Most researchers often use the same input features to compare the performance of various neural network models. However, because most models are regarded as black-box models, different methods may show different dependencies on specific features given the inherent differences in their internal structures. And the corresponding optimal inputs of different neural network models should be different. Therefore, comparing the differences in optimized input features for different neural networks is essential. This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. The experiment aims to systematically evaluate how changes in health feature types, dimensions, and data volume affect the performance of different methods and find the optimal input for each method. The results demonstrate that each network has its own optimal input in the aspects of health feature types, dimensions, and data volume. Moreover, under the premise of obtaining more accurate prediction accuracy, different networks have different requirements for input data. Therefore, in the process of using different types of neural networks for battery capacity prediction, it is very important to determine the type, dimension, and number of input health features according to the structure, category, and actual application requirements of the network. Different inputs will lead to larger differences in results. The optimization degree of mean absolute error (MAE) can be improved by 10–50%, and other indicators can also be optimized to varying degrees. Therefore, it is very important to optimize the network in a targeted manner.