Whether pharmacokinetic (PK)/pharmacodynamic (PD) target attainment for meropenem measured in plasma is a clinically reliable surrogate for target attainment in the epithelial lining fluid (ELF) in pneumonia is unclear. The objective of the current study was to characterize target attainment in plasma and ELF using prospectively collected samples and to define predictors of optimal target attainment.vs. PK/PD goals of 100% T>1xMIC and 100% T>4xMIC for a susceptible MIC of 2 mg/L.Individual Bayesian meropenem plasma and ELF profiles were generated using Pmetrics for the first 24 hours of treatment. First 24-hr PK/PD target attainment was evaluated plasma, and VELF estimates were 6.2 L/hr, 45.7, and 28.3 L. For the 100% T>4xMIC goal the following target attainment groups were identified: optimal (≥95%) in plasma and ELF , near-optimal ( >50% and < 95%) in both plasma and ELF , suboptimal (< 50%) in ELF only , suboptimal (< 50%) in plasma only , and suboptimal (< 50%) in both ELF and plasma . Loading doses significantly improved the likelihood of optimal vs. suboptimal target attainment in ELF and plasma  but did not reliably discriminate other groups.Sixty-seven patients contributed ELF and plasma PK data (range: 1-3 samples/patient for each matrix). Mean population CL, VIndividual observed meropenem (ordinate) and model predicted meropenem (abscissa) concentrations in plasma (A) and ELF (B) in patients with pneumonia.Optimal target attainment was achieved in only 40% of patients while nearly 20% of patients had suboptimal attainment in both plasma and ELF. Use of plasma as a surrogate for ELF would miss up to 20% of patients with suboptimal ELF attainment, suggesting that therapeutic monitoring of ELF may be required in some patients to optimize PK/PD attainment.Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|ASHP: Honoraria|Chambless, Higdon, Richardson, Katz & Griggs, LLP: Expert Testimony|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Guidepoint Global: Honoraria|Hall, Booth, Smith, P.C.: Expert Testimony|Merck: Advisor/Consultant|Reminger Co., L.P.A: Expert Testimony|Spero: Advisor/Consultant|Takeda: Advisor/Consultant|Taylor, English, Duma, LLP: Expert Testimony|Third Pole Therapeutics: Advisor/Consultant Michael N. Neely, MD, Astellas Pharma Global Development, Inc.: Advisor/Consultant|Astellas Pharma Global Development, Inc.: Support for the present publication Richard G. Wunderink, MD, bioMerieux: Honoraria|Kariius: Clinical Evaluation Committee|LaJolla: Advisor/Consultant|Pfizer, Inc: Clinical Evaluation Committee|Shionogi: Advisor/Consultant Nathaniel J. Rhodes, PharmD MS, Third Pole Therapeutics: Advisor/Consultant


Idiopathic pulmonary fibrosis (IPF) is characterised by progressive fibrosing interstitial pneumonia with an associated irreversible decline in lung function and quality of life. IPF prevalence increases with age, appearing most frequently in patients aged > 50 years. Pulmonary vessel-like volume (PVV) has been found to be an independent predictor of mortality in IPF and other interstitial lung diseases, however its estimation can be impacted by artefacts associated with image segmentation methods and can be confounded by adjacent fibrosis. This study compares PVV in IPF patients (N = 21) with PVV from a healthy cohort aged > 50 years (N = 59). The analysis includes a connected graph-based approach that aims to minimise artefacts contributing to calculation of PVV. We show that despite a relatively low extent of fibrosis in the IPF cohort (20% of the lung volume), PVV is 2–3 times higher than in controls. This suggests that a standardised method to calculate PVV that accounts for tree connectivity could provide a promising tool to provide early diagnostic or prognostic information in IPF patients and other interstitial lung disease. Identifying patients who are the most at risk of rapid disease progression is critical, yet the course of the disease for individual patients remains difficult to predict. Assessment of disease severity and prognosis has been guided by clinical measurements of lung function and visual scoring of imaging. However, these are not sufficiently sensitive to predict patient-specific change in lung function or progression of disease on imaging over time. New objective and repeatable measures are therefore needed.Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease (ILD) that is characterised by progressive fibrosing interstitial pneumonia and an associated progressive and irreversible decline in lung function and quality of life. The time to diagnosis of IPF from onset of first symptoms is more than 12 months in about half of patients3. Similarly, interstitial features identified using the automated tissue texture analysis software ‘CALIPER’ have been shown to have greater prognostic accuracy than visual CT scoring4. In particular, the extent of honeycombing and estimation of pulmonary vessel-like volume (PVV) were found to be independent predictors of mortality in IPF. Estimated PVV has also been shown to be associated with diffusion limitation for carbon monoxide (DLCO) in systemic sclerosis-associated ILD, and to be a strong predictor of outcome in unclassifiable ILD5. Several contributing mechanisms have been suggested for the larger PVV in patients who have worse prognosis4: (1) diversion of blood flow to areas of tissue that appear normal or mildly abnormal resulting in sustained recruitment and distension, (2) dilation of blood vessels due to increased tethering pressure on the vessels from a stiffer lung tissue, and (3) increased pleuro-parenchymal and broncho-pulmonary arterial anastomoses6. It has further been suggested that vascular alterations in IPF could be the first pathological lesions upon which fibrosis builds idiopathically11. Pulmonary hypertension (PH) is often a late stage feature of IPF12 and is well known to worsen prognosis across a range of ILDs13. Higher blood pressures in PH could theoretically increase PVV via vessel distension, however PVV does not appear to be associated with PH in the IPF cohorts that have so far been studied14.Objective quantitative analysis of thoracic high-resolution computed tomography (HRCT) imaging has shown promise for patient prognosis and staging. For example, recent studies using automated texture-based quantitation of lung fibrosis on HRCT have found that early structural changes on HRCT in IPF are predictive of decline in lung function16. The healthy lung experiences changes to the tissue structure and vasculature with older age that could influence PVV17. Therefore, quantifying the difference between the normal healthy older lung and IPF is important for understanding the relevance of PVV to a lung disease that is largely prevalent in older people. This will also provide a first step towards identifying the specific mechanisms that are responsible for increased PVV in IPF and other ILDs. Further, it is likely that the estimation of PVV depends on the specific image processing methods and assumptions that are used in its derivation. For example, reticular abnormalities can appear as vessel-like structures18, and arteries or veins that are adjacent to each other could be counted as a single vessel with a large cross-sectional area on a 2D image. It is not clear whether these artefacts markedly influence the estimation of PVV or the interpretation of its association with prognosis. To address these two limitations, in the current study we compare estimated PVV between patients diagnosed with IPF and healthy controls aged 50–93 years. An over-50-years control group is selected for consistency with the average age of the IPF cohort. Estimates of PVV and its sub-volumes are compared between a method that does not explicitly exclude segmentation artefacts, and a method that assesses only connected, branching vessel-like structures. The correlation of PVV with the extent of fibrosis and measurements of percent predicted lung function is also analysed. Data from these analyses are provided in the Supplementary Information The mean age of IPF patients is 65 yearsVolumetric HRCT imaging and pulmonary function test (PFT) data were acquired retrospectively from patients diagnosed with IPF and from a study of healthy controls. 21 patients diagnosed with IPF were recruited from Auckland City Hospital, Auckland, New Zealand. Patients were recruited consecutively as they attended routine clinical appointments. The study included patients with stable coronary artery disease (6), mild or stable mixed aortic valve disease and normal cardiac function (2), and one that developed ischaemic cardiomyopathy during the course of their IPF disease.19 with approval from the Northern Health and Disability Ethics Committee  were included as controls. Control subjects were aged over 50 years, had no smoking history, and normal lung function by ATS/ERS criteria20. IPF and control demographic and physiological data are given in Table All experimental protocols and use of data were approved by the Northern Health and Disability Ethics Committee . Data acquired as part of routine diagnostics and follow-up were used in the current study. Each IPF patient had baseline imaging and up to two follow-up scans, providing a total of 43 imaging datasets. PFTs were acquired at more frequent intervals than imaging, and PFTs at the closest date to imaging were used in the current analysis . Volumetric HRCT and PFTs from 59 healthy subjects enrolled in a separate studyFor all subjects, volumetric HRCT was acquired with slice thickness of 3 mm, slice spacing 2–3 mm, pixel resolution 0.418–0.824 mm, and reconstruction matrix 512 × 512. The total number of image slices ranged from 78 to 194. All imaging was acquired in the supine position at end inspiration without contrast enhancement. Images from control subjects were acquired on two scanners . IPF patients were scanned in multiple centres using Siemens, Philips and GE Medical Systems. To maintain the similarity of grain noise and edge enhancement of the images, for both cohorts only data that used comparable reconstruction kernels were used. These were i31f, i40f, B31f, B40f, and B41f for Siemens, Standard and Soft recons for GE, and soft tissue (B) for Philips.21 was used to automatically segment the lung surface, airways, and vessel-like structures, and to classify the lung tissue texture. Airway and vessel-like structures were identified as part of the automated software workflow so that they could be removed prior to the tissue texture analysis step. The vessel-like segmentation uses an enhancement filter based on the Hessian matrix to identify tubular structures, which is a widely used method23. Following removal of airway and vessel-like structures, each remaining image voxel was classified as normal, honeycomb, ground-glass, reticular, severe low attenuation area (LAA), moderate LAA, or mild LAA. The volume of tissue in regions identified as honeycomb, ground-glass, and reticular was summed and divided by the total imaged lung volume to give percent fibrosis by volume.The automated tissue texture classification software CALIPER2), PVV10  and PVV10 + . Volumes were calculated by multiplying the cross-sectional area in the image by the image slice thickness. This method has been used in prior studies to estimate PVV24. The volumes calculated using this method are referred to here as using ‘unfiltered’ data. Using cross-sectional area on a 2D image to partition the vessel-like structures essentially assumes orthogonality of the vessel direction to the image plane; and, using slice thickness as the multiplier for volume assumes that every structure in a plane is continuous through an image slice. The second estimation method aimed to minimise error from non-orthogonal vessels and non-continuous structures. 3D connected component thresholding and morphological operations were used to produce a graph-based structure that retains connected branching vessels and neglects small unconnected clusters of voxels. The centerlines of the vessels were extracted using the Python-based library ‘scikit-image’25. Unconnected clusters of voxels (presumed to include misclassified reticular pattern) were removed using conditional voxel removal by Euclidean distance transform26 and the Hann window27 of the lung mask. The ‘denoised’ centerline was used to generate connected graphs, i.e. connected tree-like structures. The bifurcation points between vessels were defined as nodes and the line joining two nodes was defined as a connecting edge. The radius for each vessel branch was calculated using the Euclidean distance transform matrix of the filtered vessel-like mask. Thus, the whole vessel mask was converted into a connected graph. The Python-based open source library ‘networkX’28 was used for this purpose. Vessel-like volume estimates using the second method are referred to as ‘graph-based’.Pulmonary vessel-like volume (PVV) and three vessel-like sub-volumes were estimated using two methods. The first used the vessel-like structures from the primary image segmentation and summed the volume of structures based on their cross-sectional area within each image plane to give PVV5 .Linear mixed effects models (LMM) were used for statistical analysis to account for the random effect in the IPF cohort caused by acquiring data at multiple timepoints in the same patients. The dependence between baseline and follow-up imaging was accounted for using LMM-implemented variance components. LMMs account for dependence between observations measured on the same individuals and, as the case may be, during the same visit. Variance was estimated using the classical sandwich estimatorPVV, PVV5, PVV10, and PVV10 + estimates were compared between the unfiltered and graph-based methods, and between the IPF and control cohorts. Raw standard deviations were obtained from the square rooted sum of the within- and between-participant variance in the IPF cohort. LMMs were used to compare the means of the PVV and PVV sub-volume estimates across cohorts as well as between estimation methods, using the visit number nested under the participant as random intercepts. Graph-based PVV sub-volume estimates were regressed on graph-based PVV estimates in interaction with the cohort, with participants as random intercepts. Graph-based PVV estimates were regressed on fibrosis extent and measurements of lung function in interaction with the cohort, with participants as random effects.p < 0.05).The IPF cohort had a higher proportion of males than the control cohort, which is consistent with the higher incidence of IPF in males. Results for PVV estimates by sex are given below. Age and BMI were not different between controls and IPF Table . All perp < 0.001). The unfiltered estimated PVV values were 164.3 ± 68.6 mL and 63.9 ± 17.3 mL for IPF and controls, respectively, compared with graph-based estimates of 60.9 ± 33.4 mL and 22.0 ± 6.8 mL  .Figure p < 0.001 for all). The sub-volumes as a proportion of estimated PVV were each different between the two methods: PVV10 + , PVV10  and PVV5 .The absolute values for all PVV sub-volume estimates Fig.  were smaThe results in the following sections are for PVV estimated using the graph-based method only.p values were 0.002 (PVV5), < 0.001 (PVV10 and PVV10 +) for absolute values, and only PVV5 was different (p = 0.02) for relative values.Table Estimated PVV had positive relationships with all the subvolumes. Table p < 0.001). The control cohort had a very small amount of tissue automatically classified by CALIPER as ground glass (1.5 ± 2.4%), but which in this cohort is likely to be atelectasis, small areas of motion artifact, or other lung features that are within physiologic limits. A small positive relationship between estimated PVV and ‘fibrosis’ extent was found in this group , and the proportion was lower than proportional lung tissue classified as fibrosis in IPF at baseline . For the PVV sub-volumes, a relationship with fibrosis extent was only apparent for PVV10 +  and PVV10 : there was no relationship with PVV5.Estimated PVV had a positive relationship with fibrosis as a proportion of total lung volume in IPF  measurements of lung function is shown in Fig. LCO% predicted) measurements were tested. For both controls and IPF, a relationship was found only for PVV with DLCO%  whereas there was no difference in IPF . PVV as a percentage of lung volume (PVV/LV) was also smaller in the control females than males  but not different in IPF . PVV and PVV/LV were larger in IPF males than controls (both p < 0.001). For females, the mean values of PVV and PVV/LVV were larger in IPF, however the differences were not significant .Estimated PVV was smaller in females than males in the control group  on HRCT and patient outcome for a range of ILDs5, finding that PVV has potential prognostic value. Translation to use as a clinical biomarker requires understanding how PVV might differ between IPF and healthy older people. Our study compared estimates of PVV and its sub-volumes between IPF and control data from a healthy cohort of the same age range. We found that estimated PVV was more than twice as large in our IPF cohort than in controls, and was most strongly associated with extent of fibrosis but also associated with DLCO%, FVC%, and TLC%. Removal of very small unconnected vessel artefacts reduced PVV similarly in both cohorts, but did not reduce the strength of relationships with physiological variables or extent of fibrosis.The pulmonary circulation remodels in response to diseases of the lung tissue, as well as to diseases of the vasculature and airways16. Healthy aging is itself associated with a number of changes to the respiratory system, some of which potentially affect the pulmonary vascular volume. These include increased vascular stiffness and decreased capillary blood volume, both of which increase resistance and can cause increased blood pressure. Studies have shown association between PVV and fibrosis in IPF and other ILDs, but have not compared with healthy controls. It was therefore important to assess whether PVV quantified from HRCT is associated with age, and whether PVV in IPF is different from controls with a similar age profile. The physiology of the IPF cohort and controls were statistically distinct, with lower %predicted values of FVC, FEV1, TLC, DLCO , and KCO (p = 0.015) in the IPF cohort, consistent with the impact of the disease on lung function.IPF has age-related prevalence, appearing most frequently in patients aged > 50 yearsLCO%  which could cause distension of the larger vessels. Hypoxic pulmonary vasoconstriction in poorly ventilated regions could be a further factor in increasing pulmonary vascular resistance (PVR). A recent analysis of the prevalence of pulmonary hypertension (PH) in IPF suggests that more than 70% of IPF patients have mean PAP (mPAP) greater than 20 mmHg, but only 10% have severe elevation of mPAP to greater than 35 mmHg33. It is therefore unlikely that elevation in PAP by itself explains the large difference in PVV between controls and IPF that we have observed. Conducting airway volume  also increases in IPF, but not to the same extent as PVV. The more compliant vessel wall is likely to be a factor in this difference. Plantier et al.34 found an approximately 50% increase in VD normalised by predicted TLC in an IPF cohort compared with controls , but this was independent of DLCO or disease severity. Interaction between the tissue and vessels or airways is therefore important for increasing PVV and VD, but it appears that the airways and vessels have some different mechanisms driving their volume increase. The extent of fibrosis in our cohort at baseline was approximately 20% of the lung volume. Elevations in PVV estimates were not restricted to areas of the lung where fibrosis was present , but was larger than in normal controls across the whole lung (data not shown). A large proportion of vessels with higher than normal volume are therefore surrounded by tissue that is ‘radiologically normal’.It is not clear whether the correlations between PVV estimate and D4. Our graph-based method was used to determine whether removing small unconnected imaging features and estimating vessel volume using the radius orthogonal to the vessel centreline affects the interpretation of PVV in IPF. We found that estimated PVV was considerably smaller when only the connected tree was retained compared with ‘unfiltered’ data, but the proportional reduction in IPF and controls—and across the sub-volumes—was generally similar  vessels. The graph-based PVV5/PVV was smaller than the unfiltered PVV, which is reasonable considering that PVV5 is the volume most likely to be impacted by filtering small imaging features. This is also the volume that is most likely to include reticular artefacts. Jacob et al.4 repeated their PVV analysis with PVV5 removed, finding that PVV > 5 had strong prognostic value. Our results are consistent with this, finding significant associations between PVV and physiological measurements when filtering artefact from all of the sub-volumes. It is important to note that our filtering is a relatively aggressive approach that removes all small unconnected features. Some of these are likely to be ‘valid’ vessels for the estimation of PVV, partly explaining the much smaller volumes from our filtered compared with raw data.Accurate segmentation of pulmonary vessels is challenging, particularly in IPF. Previous studies that have shown association between PVV and patient outcome have therefore used a pragmatic approach, estimating PVV from a vessel segmentation that is performed to facilitate the removal of vessel-like structures from the images prior to tissue texture classification. Pulmonary vessel-like volumes were then calculated by summing the product of slice thickness and 2D cross-sectional area of vessel-like structures on each imaging slice. This summation method potentially introduces error for vessels that are not close to orthogonal to the imaging plane. That is, the cross-sectional area will be overestimated in proportion to the difference from orthogonal; this can give rise to mis-classification by PVV sub-volume. Additionally, it is possible for reticular pattern to be misclassified as vessel-like when using automated methods because reticular tissue has a similar CT intensity to blood35 and indicates prognosis37. We found a positive relationship between PVV and the percentage of the lung tissue classified as fibrosis  was included for all patients. This possibly weights the IPF cohort towards earlier stage disease, because follow-up imaging was not available for all patients. A much larger sample size with follow up imaging would be required to evaluate the relationship between PVV and time from baseline imaging.Since IPF is progressive and irreversible, the baseline and follow-up scans are not identical; however they are not independent data. Pooling all the IPF data together needed to consider the case of variance structure and normality of errors. LMM was used for this reason to consider the random effect in the data caused by the multiple timepoints. The visits of each patient were not equally spaced in time (duration between timepoints for each patient were varying). Therefore, patient ID was selected as a random effect to gain strength from the repeated measure within the IPF cohort. Additionally, empirical estimators within SAS were used to avoid the assumptions on normality of errors and the variance structure. The estimates were still asymptotically unbiased under the correct model.38. Han et al.39 found that females are more likely to report dyspnea at an earlier stage of disease (as defined by %DLCO), and have better physical health-related quality of life (HRQL) but worse emotional HRQL than males with IPF. It is not known whether there is a physiological basis to this. If the lack of difference in PVV and PVV/LV in IPF is still present when more females are included in the analysis, this might suggest a greater impact of IPF on PVV in females than males, when compared with the lower PVV/LV in female controls.A further potential limitation is that the control group had close to equal numbers of male and female whereas the IPF group had only 19% females. To understand whether this could impact on our results we calculated mean PVV and the % ratio of PVV to segmented lung volume separately for males and females in each cohort. Absolute and relative values for males in the control group were larger than females, and IPF males were larger than control males; however there were no sex differences for IPF nor between females in the two groups. The low number of females with IPF and their relatively large standard deviations are a likely factor in not reaching statistical significance. Therefore, increasing their number would potentially reveal cohort differences for females and similar sex differences in IPF to those found for controls, but would not alter the between-cohort differences found in this study. That is, the significantly larger PVV in IPF males compared with control males suggests that sex-aggregated cohort differences are not likely to be because of sex differences. Sex differences in symptoms, disease burden, and outcomes have been reported for a number of lung diseases, including IPF41. These advanced methods can provide more extensive segmentations than were used in the current study, as well as division of PVV into arterial and venous components that might provide more subtle information to connect local tissue remodelling with vascular changes. This would potentially provide a means to further standardise calculation of PVV, with a graph-based method providing connectivity and branching data that would allow calculation of PVV to a standardised anatomical level in the vascular tree, which may overcome limitations of vascular size and dilation leading to increased detection of blood vessels within the resolution of HRCT.The graph-based method discards vessel-like structures if they were not connected to the more proximal tree, which results in a lower volume of vessel-like structures being detected than when connectivity is not considered. Methods to segment the pulmonary vessels are becoming more sophisticated, for example utilising deep learning methods to automatically separate the pulmonary arteries and veinsIn conclusion, we have shown that the pulmonary vessel-like volume in IPF is different from healthy controls with a similar age range, and this difference remains distinct even when potential artefacts and unjoined sections of the vascular tree are removed from the data using a graph-based method. The extent of fibrosis in our IPF cohort was only 20% of the lung volume, whereas PVV was 2–3 times higher than in controls.Supplementary Information.


The catalytic effect of graphene on the corannulene bowl-to-bowl inversion is confirmed in this paper using a pair-wise dispersion interaction model. In particular, a continuum approach together with the Lennard-Jones potential are adopted to determine the interaction energy between corannulene and graphene. These results are consistent with previous quantum chemical studies, which showed that a graphene sheet reduces the barrier height for the bowl-to-bowl inversion in corannulene. However, the results presented here demonstrate, for the first time, that the catalytic activity of graphene can be reproduced using pair-wise dispersion interactions alone. This demonstrates the major role that pair-wise dispersion interactions play in the catalytic activity of graphene. The catalytic effect of graphene on the corannulene bowl-to-bowl inversion is confirmed in this paper using a pair-wise dispersion interaction model. Since van der Waals forces dominate the interaction between corannulene and graphene, this paper adopts the Lennard-Jones potential to determine the interaction energy between the two molecules. Here, we assume that carbon atoms on graphene are evenly distributed on its surface so we can use continuum surface approximation to model graphene. For corannulene, its three possible conformations are considered, which are concave-up bowl, concave-down bowl and flat circular structure. Two approaches to model corannulene–graphene interaction are used. The first approach considers corannulene as a collection of 30 discrete atoms (20 carbon and 10 hydrogen atoms), and so the total energy is obtained by summing 30 pairwise interaction energies between each atom on corannulene and a graphene sheet. In the second approach, due to its geometry, we model corannulene as a collection of four circular rings (three carbon rings and one hydrogen ring) centred on the same axis , and on In the following section, we give mathematical background for the two approaches to model corannulene–graphene interactions. Detailed calculation of the integrals involved are provided in Appendices A and B. Numerical results for the interaction energies are shown in Section 3 for the three conformations of corannulene. These results are also confirmed by molecular dynamics (MD) and density functional theory (DFT) simulations which their detailed set-ups are given in Appendices C and D, respectively. Finally, concluding remarks is provided at the end of Section 3.2ρij is the distance between atoms i and j, Aij and Bij are the attractive and repulsive constants, respectively. We note that Aij = 2εijσij6 and Bij = εijσij12 where  is the energy well depth and σij = (σi + σj)/2 is the van der Waals diameter. In this paper, the van der Waals parameters for carbon (C) and hydrogen (H) are taken from Rappe et al.25 where εC = 0.4393 kJ mol−1, σC = 3.8510 Å, εH = 0.1841 kJ mol−1 and σH = 2.8860 Å. Thus, the Lennard-Jones constants Aij and Bij can be evaluated as given in Due to its simple form, the Lennard-Jones potential is commonly employed to determine the interaction energy between two non-bonded atoms, which is given byi on the first molecule and atom j on the second molecule, which is given byρij is the distance between atoms i and j.In a fully discrete approach, the total interaction energy between two non-bonded molecules can be obtained by summing the pairwise potential energy ρ denotes the distance between typical surface elements dS1 and dS2 on the first and second molecules, respectively. The constants η1 and η2 are the mean surface atomic densities of the two molecules.Another method to model the interaction between two non-bonded molecules is known as a continuum approach. This approach assumes that atoms on each interacting molecule are uniformly distributed over its entire surface of the molecule. Thus, the double summation in 26The advantage of using η is the surface density of atoms on the regular shaped molecule, ρi is the distance between a typical surface element dS on the continuous molecule and atom i on the molecule which is modelled as discrete.In the interest of modelling an irregularly shaped molecule interacting with a regular shaped structure, an alternative hybrid discrete-continuum approach is introduced, which is given byxy-plane. For corannulene, we first assume a fully discrete structure with 30 atoms (10 hydrogen atoms (blue) and 20 carbon atoms (black)) as shown in In this paper, we use 2.115 Mathematically, we represent an atom on a corannulene as a typical point with coordinates  as shown p, q, 0). Since there are 20 carbon and 10 hydrogen atoms on the corannulene, using ρi is the vertical distance of atom i from graphene sheet, ηg is the atomic density of graphene sheet (ηg = 0.3812 Å−2) and In(ρ)  is defined byHere, we model a corannulene as a collection of 30 discrete atoms. The coordinates of a corannulene in all three conformations can be found from Karton.In(ρ) is given in Appendix A.We note that the derivation of 2.21 to R4 , whereas R2, R3, and R4 involve non-bonded atoms. These rings are chosen since they are co-planar in the equilibrium (bowl-shaped) and transition state (flat) structures of corannulene. Since the positions of atoms on a corannulene are as shown in o R4 see . We noteηj denotes the atomic density of the ring Rj, Aj = AC–C and Bj = BC–C when j = 1, 2 and 3 and Aj = AC–H and Bj = BC–H when j = 4. The integral Jn(ρ) (n = 3 and 6) is defined byρ in ρ is the distance from graphene sheet to ring R1 and the distances from graphene sheet to rings R2, R3 and R4 are given by ρ + δ1, ρ + δ1 + δ2 and ρ + δ1 + δ2 + δ3, respectively. Similarly, for a concave-down bowl ρ is measured from graphene sheet to the closet ring of corannulene, which is R4 in this case. The distances from graphene sheet to rings R3, R2 and R1 are given by ρ + δ3, ρ + δ3 + δ2 and ρ + δ3 + δ2 + δ1, respectively. Finally, for flat circular shaped corannulene, all rings are concentric and have the same vertical distance ρ from the graphene sheet.Mathematically, the problem reduces to finding the interaction energy between a ring and a graphene sheet as shown in In the next section, we plot the interaction energies for the three conformations of corannulene interacting with a graphene sheet. The results are also benchmarked with molecular dynamics simulations.3ρ which is the closest distance between corannulene and graphene sheet. We obtain identical results for both discrete and continuous ring approaches, which are plotted as solid lines in ρmin) for each conformation of corannulene that minimises the interaction energy of the system. The values of ρmin and the corresponding minimum energy are given in −1, whereas the interaction energy between the concave-up bowl and concave-down bowl and the graphene sheet amount to 129.7 kJ mol−1 and 167.3 kJ mol−1, respectively. Thus the pair-wise dispersion interactions between the planar graphene sheet and the planar transition structure are stronger by 48.9 and 11.3 kJ mol−1, respectively, than the concave-up and concave-down structures. This result is due to the closer proximity of the carbon atoms of corannulene and graphene in the planar transition structure than in the concave-up and concave-down structures.Here, the interaction energy is determined as a function of 15,17 In order to compare on an even keel between the interaction energies obtained using our pair-wise dispersion model we need to calculate the DFT interaction energies on the electronic potential energy surface. For this purpose, we performed DFT calculations on the electronic potential energy surface using the PW6B95-D4 functional (see Appendix D for further details). At the PW6B95-D4/def2-TZVPP level of theory with basis set superposition error (BSSE) corrections, we obtain the following interaction energies between corannulene and graphene 82.6 (concave-up bowl), 111.3 (planar TS), and 87.1 (concave-down bowl) kJ mol−1. Using a larger quadruple-ζ basis set without BSSE corrections, namely at the PW6B95-D4/def2-QZVPP level of theory, we obtain similar interaction energies of 87.1 (concave-up bowl), 117.1 (planar TS), and 91.6 (concave-down bowl) kJ mol−1. There is little to choose between the two levels of theory since both have different advantages and disadvantages in terms of basis-set completeness. However, the differences of 4.4–5.8 kJ mol−1 between both sets of results indicate that we are only a few kJ mol−1 away from the complete basis set limit. Here, we will focus on the results obtained with the larger def2-QZVPP basis set. The pair-wise dispersion model predicts much larger interaction energies of 129.7 (concave-up bowl), 178.6 (planar TS), and 167.3 (concave-down bowl) kJ mol−1  as a location of an atom on corannulene interacting with a graphene sheet lying on the xy-plane on which its typical point has coordinates . To determine the interaction energy between a single atom and a graphene sheet using the Lennard-Jones potential, we introduce the integral In(ρ) which is given asS is the surface element of graphene sheet and d is a typical distance between atom on the corannulene and graphene sheet  so that Next, we substitute consequently, SinceM = ρ tan ψ where ψ ∈ , we obtainwhich yieldand upon simplifying gives By further substituting r with typical point  interacting with a graphene sheet lying on the xy-plane on which its typical point has coordinates . To determine the interaction energy between a ring and a graphene sheet using the Lennard-Jones potential, we introduce the integral Jn(ρ) which is defined byS is the surface element of graphene and d is the distance between typical points on the ring and graphene sheet  software packageζ def2-nZVPP basis sets .28,29 Where the recently developed, atomic-charge dependent D4 dispersion correction is employed.30,31 The PW6B95 exchange-correlation functional has been extensively benchmarked and found to be robust for both reaction energies and barrier heights involving related systems.18,20,32–38 We were able to perform basis set superposition error (BSSE) calculations in conjunction with the def2-TZVPP basis set.39–41 However, the BSSE calculations in conjunction with the def2-QZVPP basis set proved beyond our computational resources. All optimized geometries were taken from ref. 42Density functional theory (DFT) calculations were performed using the hybrid meta-GGA  DFT method PW6B95-D4 in conjunction with the triple- and quadruple-There are no conflicts of interest to declare.


Sarcoidosis is an inflammatory and granulomatous disease of uncertain etiology that can impact various organ systems and exhibits diverse clinical presentations, which adds to the complexity of disease diagnosis and management. Pathologically, it is distinguished by the presence of noncaseating granulomas within the affected organ system. In this case report, we describe a 34-year-old Caucasian female patient with isolated splenic and possible hepatic involvement of sarcoidosis, presenting with severe abdominal pain. The absence of the typical pulmonary, cutaneous, or joint involvement posed challenges in achieving a definitive diagnosis and determining the appropriate management. Imaging studies revealed hepatic and splenic hypodensities, necessitating consideration of various differential diagnoses, including lymphoproliferative disorders, immunological disorders, environmental particle exposure, infectious causes, neoplasms, and drug reactions. The severity of symptoms in this case required hospital admission for pain and nausea control, biopsy, and eventual splenectomy with pathology that confirmed the diagnosis of splenic sarcoidosis. Sarcoidosis, a multisystem inflammatory disease, was initially identified and named over a century ago. In 1878, Sir Jonathan Hutchinson first described the condition of sarcoidosis . While iThis article was previously presented as an abstract at the 2023 WV Society of Hospital Medicine Abstract Competition in May 2023.Cytomegalovirus, Human immunodeficiency virus, Epstein-Barr virus, hepatitis panel, acid-fast bacilli, Bartonella, Chlamydia trachomatis, Chlamydia psittaci, Treponema pallidum, enteric bacteria profile, Lyme disease, Coxiella, Cryptococcus, Histoplasma capsulatum, Blastomyces dermatitidis, ova and parasites, and Toxoplasma gondii. Additionally, an autoimmune workup was conducted, involving testing for systemic lupus erythematosus, mixed connective tissue diseases, Sjogren's syndrome, scleroderma, polymyositis, alpha-1 antitrypsin, and ceruloplasmin. The comprehensive workup yielded unremarkable results, except for an elevated C-reactive protein level of 4.2 mg/dL (reference range <0.9 mg/dL). Additional imaging studies, including CT of the chest and CT of the head, did not reveal any significant findings. The patient received symptomatic management and was referred to a tertiary care facility for further workup, which was also unrevealing. Despite extensive workup and symptomatic management, she continued to have persistent abdominal pain. She was then admitted to the hospital, where a repeat liver biopsy was performed via endoscopic ultrasound, this was inconclusive with some rare noncaseating granulomas. Universal PCR analysis was repeated, and the results were negative for fungal DNA or mycobacterium DNA. Sarcoidosis was suspected, prompting initial treatment with a prednisone taper. However, the patient did not tolerate the steroids, due to significant anxiety, agitation, and other adverse side effects. Although antibiotics were tried based on infectious disease recommendations, no definitive diagnosis had been made at that time, and this failed to provide relief for the patient. Consequently, the patient opted for splenectomy, and the subsequent pathology report confirmed the suspected diagnosis of sarcoidosis . Contrast-enhanced abdominal and pelvic computed tomography (CT) displayed multiple low-attenuation lesions in the liver and spleen Figure . A liverDue to its intricate characteristics, the potential for multiorgan involvement, and the absence of pathognomonic features, sarcoidosis is primarily considered a diagnosis of exclusion. The diverse range of clinical presentations associated with sarcoidosis can present challenges in both diagnosing and managing the disease. While sarcoidosis was suspected in this case, the involvement of splenic and hepatic lesions with an inconclusive biopsy necessitated a comprehensive differential diagnosis, encompassing lymphoproliferative disorders, immunological disorders, environmental particles exposure, infectious causes, neoplasms, and drug reactions . ExtrapuIsolated splenic sarcoidosis represents a rare manifestation of sarcoidosis, which poses challenges in terms of diagnosis due to the absence of a definitive diagnostic test. Instead, the diagnosis relies on the presence of clinical and radiographic manifestations consistent with sarcoidosis, exclusion of other diseases, and histopathologic identification of noncaseating granulomas. The treatment of sarcoidosis often involves the administration of steroids; however, in this specific instance, the patient exhibited poor tolerance, thereby requiring alternative therapeutic approaches. Improving care for patients with sarcoidosis often requires multidisciplinary teams working together to understand the unique patient presentation and affected organ system, to maximize the diagnosis and treatment for the safety and quality of life of the patient.


The design and development process of wind velocity sensors for mining has been a challenging task due to the complexity of a large number of field tests. To resolve this problem, this study aimed to provide a comprehensive test device for the design and development of high-precision wind velocities sensor for mining. Through a combination of experiments and computational fluid dynamics (CFD), a device that can simulate the mine roadway environment was developed. The device can control the temperature, humidity, and wind velocity parameters to fully replicate the mine roadway environment. It gives designers and developers of high-precision wind velocity sensors for mining a rational and scientific testing environment. In order to quantitatively define the uniformity of air flow in the mine highway section, the research introduced the non-uniformity determination method. The approach was expanded to assess the cross-sectional uniformity of temperature and humidity. The wind velocity within the machine can increase to 8.5 m/s by selecting the right kind of fan. The minimum wind velocity non-uniformity at this moment is 2.30%. The device’s internal temperature can be raised to 38.23 °C and its humidity level can be increased to 95.09% by carefully crafting the rectifier orifice plate’s structure. At this time, the lowest temperature non-uniformity is 2.22%, and the lowest humidity non-uniformity is 2.40%. The device’s average wind velocity is 4.37 m/s, its average temperature is 37.7 °C, as well as its average humidity is 95%, per the emulate results. The device’s non-uniformity in wind velocity, temperature, and humidity is 2.89%, 1.34%, and 2.23%, respectively. It is capable of simulating the mine roadway environment in its entirety. The ventilation system is the fundamental system to ensure the safety of mine production. According to statistics, many accidents are caused by ventilation system breakdowns that seem to go unnoticed in time . It is cCreating the mimicking test equipment is premised on mastering the mine roadway’s environmental parameters. The wind velocity, temperature, and humidity parameters of mine roadway air flow have already been tested and simulated by many scholars. They believe that the wind current in the mine roadway is basically distributed in parallel streamlines and that the wind velocity is distributed evenly in the same vertical section . The tunBased on the findings of the aforementioned investigation combined with the environmental parameters of the mine roadway measured data, the wind velocity, temperature, and humidity coupling device model was designed and constructed for this investigation using the CFD approach. For a complete simulation of the tunnel environment, it can control the wind velocity, temperature, and humidity parameters simultaneously. In a bid to quantitatively determine the uniformity of wind flow in the mine roadway section, the study applies the non-uniformity determination method . The appThe device’s three-dimensional model was created using AutoCAD software and can be seen in 1 = 620 mm, length L1 = 400 mm, impeller diameter D = 600 mm; the diameter of humidification pipe section D2 = 50 mm, length L2 = 100 mm; the length of the heating pipe section L3 = 200 mm, and the diameter was adjusted according to the rectifier effect of the rectifier orifice. The test pipe sections were square pipes with side length a = 500 mm and length L4 = 1000 mm. The origin of the device coordinate was located at the center of the test pipe section near the rectifier orifice plate. yOz plane; monitoring surface A was located at x = 0, while monitoring surface B was located at x = 1000.The specific dimensions of the device were: fan diameter DSST k-ω Turbulence model, the internal flow field and heat transfer of the device model met the following equations:Fluent, as a leading CFD software with a wide audience, was used to solve the model calculation of this study. The simulation process adopted double precision three-dimensional solver and The mass conservation equation isThe momentum conservation equation isThe energy conservation equation isu, v and w—components of fluid velocity in x, y and z directions, respectively, m/s;P—pressure, Pa;μ—dynamic viscosity, Pa·s;T—temperature, K;pC—specific heat capacity at constant pressure, J/(kg·K);λ—thermal conductivity, W/(m·K).SST k-ω. For turbulence model, the energy equation was opened, and the convergence condition was that each residual error was less than 10−6. In the process of simulation, such assumptions were made: ① The air flow can flow uniformly at the inlet and outlet of the model, and the physical parameters will not change; ② the fluid is fully developed in the simulation process; ③ viscous dissipation between fluids is considered; ④ the fluid is incompressible Newtonian fluid; ⑤ ignore gravity effects.Meshing was used to mesh the above three-dimensional model, and the mesh independence was verified. The rotating part of the axial flow fan was treated with sliding mesh. The rotating velocity of the axial flow fan was 1450 r/min according to the actual operating parameters of the fan. The ambient temperature was 25 °C and the humidity was 40%. The air flow was not heated and humidified, the hole diameter of the rectifier orifice plate was 40 mm, and the hole arrangement was straight. Turbulence model using The distribution of wind velocity, temperature, and humidity in the middle section of the mine roadway was basically uniform. Therefore, this paper introduced the non-uniformity judgment method to quantify the simulation and experimental results by calculating the non-uniformity of wind velocity, temperature, and humidity. Taking the wind velocity non-uniformity as an example, the wind velocity non-uniformity can quantitatively describe the flow field distribution at the test section of the device. The formula for calculating the wind velocity non-uniformity is shown in Equation (4). The calculation formula of the temperature and humidity non-uniformity refers to the calculation formula of the wind velocity non-uniformity.When the non-uniformity does not exceed 25%, it can be considered that the flow field inside the device is uniform. Therefore, the non-uniformity of wind velocity, temperature, and humidity of the device shall be less than 25%. Furthermore, the measuring points were located on monitoring surfaces A and B, and took 16 uniformly distributed measuring points, respectively.Q is where, in ideal conditions:The mine roadway’s main inlet and return air velocities must not exceed 8 m/s in accordance with mine safety regulations. Therefore, the device’s wind velocity must not be less than 8 m/s when the inlet fan is operating. The test pipe section has a side length of 500 mm square pipe. The air volume calculation formula says that the required air volume Q—air volume, m3/h;S—sectional area of air duct, m2;v—wind velocity, m/s.Under ideal circumstances, it is necessary to use the safety factor to adjust the air volume so that the fan has a sufficient supply of air. The safety factor, which is used frequently in fan selection and air volume design, is an increase of five to twenty percent based on the ideal air volume. The value of the safety factor should not be too high in order to cut down on the amount of money spent on equipment and running it. The safety factor of 1.15 was used in this paper to calculate the corrected air supply, which was:3/h;Q—ideal air supply, m3/h;3/h. However, the simulation analysis revealed that the wind velocity was only 6.5 m/s after employing the type A axial flow fan. This is mostly because of the fluid’s general viscosity, the loss of vortex caused by boundary layer separation, and the loss of friction between the internal components of the fan and the air flow. Additionally, there will be some space between the fan’s static components and its rotating parts. The fan will develop a high-pressure and a low-pressure region as the impeller rotates. Leakage loss will result as a result of the air flowing through these gaps from the high pressure region to the low pressure region. The test device also has a rectifier orifice plate, a humidifying pipe section, and a heating pipe section, all of which make the air resistance when passing through the device too high and prevent the wind velocity from reaching 8 m/s.The corrected air supply volume will be used to select the fan. The type A axial flow fan can theoretically satisfy the demand for air with a volume of 10,000 m3/h. As depicted in According to the simulation analysis, the pipeline’s air velocity requirements call for an type B axial flow fan with a volume of 21,500 mThe selection of a fan may be sufficient to provide the device with sufficient wind velocity; however, it is necessary to investigate the distribution of the test device’s temperature and humidity. Miners’ primary location for carrying out production tasks is the stope. In the stope, it is necessary to measure the temperature, humidity, and wind velocity. As a result, the simulating test device must be able to completely replicate the parameters of the stope environment. The mine safety regulations stipulate that the wind velocity in the stope shall not exceed 4 m/s and the wet-bulb temperature shall not exceed 30 °C. The environment in the downhole is humid, frequently exceeding 90%. To ensure that the mine stope’s environmental parameters can be replicated, the device needs to be able to achieve a heating temperature above 35 °C and a humidity above 95% at a wind velocity of 4 m/s.This method effectively avoids the non-uniform distribution of air flow caused by the phenomenon of cylindrical flow around the cylinder heating tube , due to Utilizing the breeze velocity, temperature, and mugginess non-uniformity as the assessment file, the impact of various designs of the rectifier hole plates on the lopsidedness were examined. The outcomes show that when the (f) structure with roundabout game plan, focus gap of 60 mm, corner opening of 60 mm, and other gap of 40 mm is embraced, the temperature and mugginess inside the test gadget are equally circulated. Obtained by reading section data:Based on the analysis of 2, and the flow rate of the water mist generator was 1.8 m/s. In addition, wind velocity sensors, temperature sensors, and humidity sensors were used for real-time monitoring of environmental parameters inside the device. In order to ensure that the temperature and humidity sensors could work normally in the simulated high temperature and humidity environment, the epitaxial waterproof highly sensitive temperature and humidity probe was selected. This probe can work stably at the temperature of 20~50 °C and the humidity of 20~100%. There were two sets of wind velocity, temperature and humidity sensors, which were located in the center of the two monitoring pipe sections (monitoring surface A and B). The arrangement of the device and sensors are shown in According to the results of fan selection and rectifier orifice plate optimization, a simulation testing device of mine roadway with adjustable wind velocity, temperature. And humidity was constructed. The model was based on the similarity theory ,21, and The device is tested after the device runs stably , and recAs can be seen from In this study, CFD technology was combined with an experimental design. We designed and constructed a mine roadway simulating test device with adjustable wind velocity, temperature, and humidity. The device could simultaneously control wind velocity, temperature, and humidity parameters to fully simulate the tunnel environment. The results were as follows:3/h was used in the test device. The wind velocity inside the device could reach 8.5 m/s. At this time, the minimum wind velocity non-uniformity was 2.30%.(1) Through air volume calculation and simulation analysis, it was concluded that axial flow fan with air supply of 21,500 m(2) By changing the arrangement of the rectifier orifice plate, center aperture, corner aperture, and other parameters, the structure of the rectifier orifice plate was optimized. It is concluded that when the pore arrangement mode of the rectifier orifice plate was circular arrangement, the center aperture was 60 mm, the corner aperture was 60 mm, and the other aperture was 40 mm, the wind velocity inside the device could reach 4.83 m/s, the temperature could reach 38.23 °C, and the humidity could reach 95.09%. In addition, the distribution of wind velocity, temperature, and humidity in the device was relatively uniform. The lowest non-uniformity of wind velocity was 14.17%, the lowest non-uniformity of temperature was 2.22% and the lowest non-uniformity of humidity was 2.40%.(3) According to the results of fan selection and rectifier orifice plate optimization, a simulation testing device of mine roadway with adjustable wind velocity, temperature, and humidity was constructed. The average wind velocity of the device was 4.37 m/s, and the lowest wind velocity non-uniformity was 2.89%. The average temperature was 37.7 °C, and the lowest temperature non-uniformity was 1.34%. The average humidity was 95% and the lowest humidity non-uniformity was 2.23%.The research results showed that the device can fully simulate and reproduce the wind velocity, temperature, and humidity environment of the mine roadway. It can provide a scientific and reasonable test platform for the design and development of high-precision wind velocity sensor. It is a fundamental device for improving the wind velocity monitoring technology in the mine roadway and plays an important role in ensuring the safe production of the mine.


Highly thermally conductive composite films with elimination of graphene nanosheet wrinkles prepared by a constrained drying strategy of in-plane stretching.Reveals the mechanism of graphene nanosheet wrinkle elimination to improve the thermal conductivity of composite films and demonstrates its use as a heat sink film for cooling flexible LED chips and smartphones.The online version contains supplementary material available at 10.1007/s40820-023-01252-w. π–π interactions. The in-plane mechanical stretching eliminates graphene nanosheets wrinkles by suppressing inward shrinkage due to capillary forces during drying and achieves a high in-plane orientation of graphene nanosheets, thereby creating a fast in-plane heat transfer channel. The composite films (GNS/ANF-60 wt%) with eliminated graphene nanosheets wrinkles showed a significant increase in thermal conductivity (146 W m−1 K−1) and tensile strength (207 MPa). The combination of these excellent properties enables the GNS/ANF composite films to be effectively used for cooling flexible LED chips and smartphones, showing promising applications in the thermal management of high-power electronic devices.Graphene-based thermally conductive composites have been proposed as effective thermal management materials for cooling high-power electronic devices. However, when flexible graphene nanosheets are assembled into macroscopic thermally conductive composites, capillary forces induce shrinkage of graphene nanosheets to form wrinkles during solution-based spontaneous drying, which greatly reduces the thermal conductivity of the composites. Herein, graphene nanosheets/aramid nanofiber (GNS/ANF) composite films with high thermal conductivity were prepared by in-plane stretching of GNS/ANF composite hydrogel networks with hydrogen bonds and The online version contains supplementary material available at 10.1007/s40820-023-01252-w. The continuing miniaturization and high integration of microelectronic components has brought the issue of heat dissipation into sharper focus , 2. For Graphene displays an ultra-high in-plane thermal conductivity, due to its perfect two-dimensional structure , 21. Its−1 K−1, and the tensile strength increased from 99 to 207 MPa  During in-plane stretching, the mechanical tension not only counteracts the contraction effect of the capillary force, but also spreads and flattens the flexible large-size graphene nanosheets along the in-plane direction. This process eliminates the wrinkles of graphene nanosheets, as well as the air bubbles introduced by the wrinkles, making the structure of GNS/ANF composite films more compact. The oriented and ordered stacking of GNS is favorable for the rapid phonon transfer along the plane. (ii) There is a strongHigh-quality graphene nanosheets  were purchased from Deyang Carbenene Technology Co., Ltd. AMF (Kevlar 29) was purchased from Dongguan SOVETL Co., Ltd. Potassium tert-butoxide was provided from Sigma-Aldrich, and dimethyl sulfoxide  was used as received.Constrained dried GNS/ANF films were prepared by in-plane stretching of GNS/ANF composite hydrogels. Six grams of AMF and 4.5 g potassium tert-butanol were added to 189.5 g dimethyl sulfoxide, and the mixture was mechanically stirred for 12 h to obtain dark red, viscous ANF solution (3 wt%). GNS (0.75 g) was added to dimethyl sulfoxide solution (32.58 g) and dispersed by sonication for 2 h . Then, 3 wt% ANF solution (16.67 g) was added and mechanical stirring was continued for 12 h to form uniformly dispersed GNS/ANF-60 wt% sol. The GNS/ANF sol was poured into a Petri dish and then immersed in water to form a black GNS/ANF hydrogel, which was rinsed with repeatedly using deionized water to completely remove dimethyl sulfoxide and potassium tert-butanol. The mass fraction of GNS in the composite hydrogel was 1.5 wt% and the mass fraction of ANF was 1 wt%. The GNS/ANF hydrogels were stretched in-plane with a stretching device and kept under stretching constraint until drying, as shown in Fig. 2. Raman spectra were recorded by microscopic confocal Raman spectrometer (ARAMIS HORIBA) with an excitation wavelength of 532 nm. XPS data were obtained using an X-ray photoelectron spectrometer . FTIR spectra were collected using a Nicolet IS50 FTIR spectrometer (Thermo Fisher Scientific). X-ray diffraction (XRD) patterns were obtained using a LabX XRD-6000 (SHIMADZU) instrument with Cu Kα radiation at a scanning speed of 10° min−1. Tensile properties were measured on a Shimadzu AGS-X Tester. The thermal conductivity of the nanocomposite was calculated with K, α, Cp represent thermal conductivity, thermal diffusivity, density, and heat capacity, respectively. Thermal diffusivity was measured using the laser flash method . Density was obtained by the calculation of sample volume and weight. The specific heat capacity was obtained by differential scanning calorimetry . Infrared thermal images were taken with an infrared thermal imager (FOTRIC 345). The electrical conductivity was analyzed by four-probe method . Thermogravimetric analysis (TGA) was performed with a simultaneous thermal analyzer  at a heating rate of 10 °C min−1 under air atmosphere.SEM images were obtained with a field-emission scanning electron microscope . TEM images were obtained with a transmission electron microscope . AFM images were obtained with an atomic force microscope . The test area of Fig. ID/IG = 0.07, C/O = 47.1, Fig. S1) were utilized in this study. It is worth noting that graphene nanosheets have numerous edge folds and wrinkles, which will make it difficult to achieve an orderly alignment of graphene nanosheets in composites . These results demonstrate that the in-plane stretching effectively improves the stacking orientation of graphene nanosheets and increases the tensile strength of the composite films. Interestingly, the electrical conductivity of the composite films gradually increased with the increase of the in-plane stretching ratio, indicating that graphene nanosheets with wrinkles eliminated by in-plane stretching build better conductive paths .−1 is increasingly shifted to the left toward 1605 cm−1, indicating that the π–π interaction between GNS and ANF becomes stronger. To further confirm this strengthening of the π–π interaction, we examined the XPS spectra of C 1 s. As the in-plane stretching ratio increased, the peak representing the π–π interaction gradually shifted from 290 to 290.8 eV, consistent with the Raman spectroscopy results  [−1 (C=O stretching vibration) and 3326 cm−1 (N–H stretching vibration) were observed to broaden and shift to the left  . Meanwhieft Fig. e, f, indAfter in-plane stretching, the GNS is stretched out to eliminate the folds, resulting in a tighter bond with ANF, resulting in stronger interfacial interactions. In addition, the GNS is expanded and tiled along the in-plane direction to eliminate air bubbles contained within the wrinkles. The alternate stacking of GNS and ANF in an orderly orientation forms a more compact and orderly structure, and the density of the composite films gradually increases Fig. g. All thThe excellent thermal conductivity and mechanical properties of the GNS/ANF composite films with eliminated GNS wrinkles have been demonstrated, and its application as a thermal management material for cooling flexible LED chips and smartphones will now be discussed. As shown in Fig. In addition, these films were also integrated into the internal back of the smartphone Fig. e, f, and−1 K−1), high tensile strength (207 MPa) GNS/ANF-60 wt% composite films were prepared based on the in-plane stretching strategy and sol–gel-film transformation approach. The remarkable improvement in thermal conductivity is attributed to the in-plane stretching strategy, which effectively eliminates the graphene nanosheets wrinkles, improves the orientation of GNS along the in-plane direction, and further enhances the interfacial interaction between GNS and ANF. We believe that the GNS/ANF composite films with such exceptional thermal conductivity and mechanical properties hold promising applications for various thermal management applications in the electronics and automotive industries.In summary, ultra-high in-plane thermal conductivity (146 W mSupplementary file1 (PDF 1352 KB)Below is the link to the electronic supplementary material.