Quantifying skin aging changes and characterizing its 3D structure and function in a non-invasive way is still a challenging area of research, constantly evolving with the development of imaging methods and image analysis tools. In vivo multiphoton imaging offers means to assess skin constituents in 3D, however prior skin aging studies mostly focused on 2D analyses of dermal fibers through their signals’ intensities or densities. In this work, we designed and implemented multiphoton multiparametric 3D quantification tools for in vivo human skin pigmentation and aging characterization. We first demonstrated that despite the limited field of view of the technic, investigation of 2 regions of interest (ROIs) per zone per volunteer is a good compromise in assessing 3D skin constituents in both epidermis and superficial dermis. We then characterized skin aging on different UV exposed areas—ventral and dorsal forearms, face. The three major facts of aging that are epidermal atrophy, the dermal–epidermal junction (DEJ) flattening and dermal elastosis can be non-invasively quantified and compared. Epidermal morphological changes occur late and were only objectified between extreme age groups. Melanin accumulation in suprabasal layers with age and chronic exposure on ventral and dorsal forearms is less known and appears earlier. Superficial dermal aging changes are mainly elastin density increase, with no obvious change in collagen density, reflected by SHGto2PEF ratio and SAAID index decrease and ImbrN index increase on all skin areas. Analysis of the z-dermal distribution of these parameters highlighted the 2nd 20 µm thickness normalized dermal sub-layer, that follows the DEJ shape, as exhibiting the highest aging differences. Moreover, the 3D ImbrN index allows refining the share of photoaging in global aging on face and the 3D SAAID index on forearm, which elastin or fibrillar collagens densities alone do not allow. Photoaging of the temple area evolves as a function of chronic exposure with a more pronounced increase in elastin density, also structurally modified from thin and straight elastic fibers in young volunteers to dense and compact pattern in older ones. More generally, multiphoton multiparametric 3D skin quantification offers rich spatial information of interest in assessing normal human skin condition and its pathological, external environment or product induced changes. One of the most important extrinsic factors is the chronic sun exposure, whose deleterious effects (photo-aging) can easily be seen by comparing the skin areas regularly exposed with the ones protected from sunlight. Clinically, the intrinsic or chronological skin aging results in epidermal and dermal atrophy, skin dryness, reduced skin elasticity, appearance of fine wrinkles, fragility of skin and blood capillaries, whereas the extrinsic aging is characterized by the appearance of pigmented spots, deep wrinkles and telangiectasias. Histologically, the skin aging results in decreased epidermal thickness, even though the number of cell layers remains unchanged, flattening of dermal–epidermal junction, increased mottled pigmentation, dermis atrophy and degenerative dermal changes, most obvious being elastosis in the sun-exposed areas2.Human skin aging6,  optical coherence tomography9 and multiphoton imaging17, have paved the way towards a non-invasive live 2D/3D imaging of in vivo human skin, offering means of avoiding performing skin biopsies in several situations and assessing human skin aging in vivo. While all these methods enable morphological characterization of the epidermis, epidermal cells and dermal–epidermal junction with sub-µm resolution, only multiphoton imaging provides specificity for melanin, elastin or fibrillar collagen detection. Its ability to detect intrinsic two-photon excited fluorescence (2PEF) from cellular and extracellular matrix constituents  and second harmonic generation (SHG) signals from fibrillar collagens20, leveraged by fluorescence lifetime imaging (FLIM) to provide functional information on skin constituents21, enabled different applications in dermatological sciences33.Quantifying skin aging changes especially on human face and more generally characterizing its 3D structure and function in a non-invasive way is still a challenging area of research, constantly evolving with the development/improvement of linear and non-linear optical imaging methods and image analysis tools. Methods such as confocal reflectance imaging36. Based on these data, combination parameters were introduced such as the SHG to autofluorescence aging index of dermis (SAAID) defined as the ratio of the difference to the sum of SHG and 2PEF pixels areas (2D SAAID density-based index)10 or pixels intensities (2D SAAID intensity-based index)11. The 2D SAAID intensity index generally decreases with age on forearm or gluteal regions38 and facial skin cheek area35, but a lack of aging variation of this index34 or of the mean 2PEF and SHG signal intensities17 has also been reported. Its aging decrease can be more pronounced on solar exposed areas characterized by an increased autofluorescence intensity due to elastosis. This index also depends on the gender and the investigated area6 making it necessary to select comparable sites when comparing photo-aged to sun-protected skin.Although multiphoton imaging enables 3D skin imaging, various ex vivo and in vivo human skin studies focused on assessing dermal aging by computing the mean 2PEF (mainly elastin) and SHG (fibrillar collagens) signal intensities or densities in 2D, at specific fixed depths around 110–160 µm bellow the skin surface38 could be more robust in assessing the dermal fibers changes as initially proposed by Lin et al.10. Moreover, integrating the measurements over the whole 3D imaged dermis instead of choosing a fixed z-depth plane could minimize their dependency over factors such as the native skin variability.Quantification of an intensity-based SAAID index alone cannot give information on the elastin and collagen fibers densities nor into their respective increase or decrease with aging and one must quantify these parameters individually to correctly interpret the data. Assessing, interpreting, and comparing the dermal fibers intensities results between different studies is quite tricky: one must consider factors such as the excitation power and the attenuation of both the excitation wavelength and of the emitted 2PEF and SHG signals, impacted by variations in epidermal thickness, melanin density and the presence of blood capillaries. For these reasons, density-based parameters13, opened the possibility of quantifying human skin in 3D and assessing not only its dermal fibers within the entirely imaged dermis, but also the epidermal and DEJ morphology. We reported for the first time in vivo, the ventral forearm skin aging changes in the 3D parameters of epidermal thickness (decrease), DEJ undulation (flattening), density of elastin (increase) and collagen fibers (no change), and their 3D SAAID density-based index (decrease)13. Moreover, to assess the elastin and collagen fibers’ overlapping, we introduced a multimodal 3D organization parameter 13 found to increase with aging on ventral forearm. Recently, by comparison with FLIM bi-exponential and phasor analyses, we have demonstrated how melanin can be detected in 3D by multiphoton Pseudo-FLIM analysis 39. As the epidermal thickness may vary according to anatomical sites, skin ethnicity, aging or products application, we proposed to use both a global 3D epidermal melanin density parameter and a melanin z-epidermal distribution profile allowing to compare skin regions with varying epidermal thickness.Our group, with the development of an automatic 3D epidermal-dermal segmentationIn this work, we propose to take into account all the available 3D rich spatial information offered by multiphoton imaging and perform a global 3D assessment of in vivo human skin aging changes on ventral and dorsal forearms and face temple skin areas, after addressing the question of skin constituent’s variability and robustness of 3D multiphoton parameters for skin assessment.Clinical studies were conducted in Paris, France in accordance with local legal requirements, Principles of the Good Clinical Practices, and the Declaration of Helsinki and all volunteers gave written informed consent. The experimental protocols of studies I and II were approved by the Saint Louis Hospital ethics committee . According to French regulations on non-interventional research , the experimental protocol for study III did not require ethics committee approval.40:   and  . Each age and skin color ITA group includes 5 women and 5 men. Colorimetry  measurements were performed on ventral forearm. Multiphoton imaging was performed in 4 adjacent ROIs, arbitrarily chosen within the central part of a 0.8 cm2 region (same location as for colorimetry). involved 40 European origin volunteers (20 women and 20 men) in two age groups 30–40 year-old (n = 20) and 55-65 year-old (n = 20) and with skin color determined on the ventral forearm by Individual Typology Angle (ITA)32) involved 15 young (18–25 year-old) and 15 aged (70–75 year-old) European origin female volunteers with Fitzpatrick phototypes I–IV. Multiphoton imaging was performed on ventral (2 adjacent ROIs) and dorsal (1 ROI) forearms (less exposed vs. sun exposed skin areas). involved 15 young (18–25 year-old) and 15 aged (70–75 year-old) European origin female volunteers with Fitzpatrick phototypes I to IV and skin color ITA  (ITA groups “light” and “intermediate”) on the ventral side of the upper arm at screening. Colorimetry  measurements were performed on dorsal forearm and face temple area. Multiphoton imaging was performed within 3 adjacent ROIs, on dorsal (mostly exposed) forearm and face temple (exposed) areas, the temple area being chosen in alignment with the internal and external orbital canthus.For all these studies, during data quality check, we mainly excluded the multiphoton z-stacks with important volunteer movements during acquisition. The final number of volunteers and ROIs for each study is given in “Supplementary information flex™ systems  integrating TCSPC detectors, a FLIM module  and an 80 MHz IR fs pulsed laser .Z-stacks of images were acquired using either DermaInspect® or MPT42. Image characteristics: 3D z-stacks ; 130.3 × 130.3 µm2; 511 × 511 pixels (0.255 µm/pixel) × 4 time channels (2.08 ns/time channel), 0–8.33 ns time range, maximum intensity decay at 1.33 ns; 28 µs pixel dwell time; 7.4 s/image; 9.4 min/3D z-stack.With DermaInspect®, images were acquired upon 760 nm excitation and detection in the 390–650 nm  and 380 ± 7 nm  wavelength ranges, using a 40x /1.3 NA oil immersion objective , as previously describedflex™, images were acquired in similar conditions to DermaInspect® except for the following parameters: 2PEF signal detection in the 409–650 nm; exponentially increased excitation power from 10 mW at the skin surface up to 30 mW at depths exceeding 75 µm; 2.54 µm z-step; 204,4 × 204,4 µm2; 511 × 511 pixels (0.4 µm/pixel); 16.8 µs pixel dwell time; 4.4 s/image; 5.2 min /3D z-stack of 70 images.With MPT13 to identify skin layers, characterize DEJ 3D-shape and extract quantitative parameters on skin constituents and layers /SHG images was performed with MPSTS software43 . The 3D z-stack of melanin masks and automatic epidermis segmentation are jointly used for 3D melanin quantification  is further processed for melanin detection using Pseudo-FLIM melanin analysis methodion Fig. b. Globalion Fig. b.The 3D morphology of epidermal layers, sublayers and interfaces is characterized by several parameters: mean thickness of SC, LED and epidermis  and normalized DEJ area .3D SHGto2PEF fibrillar collagen to elastin densities ratio or the 3D SAAID (the SHG-to-AF aging index of dermis). The 3D SAAID fibers density index, a straightforward generalization of 2D SAAID, is defined as the ratio between the difference and the sum of fibrillar collagen and elastin fibers densities. A mean value is computed within the superficial dermis layer or normalized dermal sublayers.We assess the fibers within the whole imaged dermis  and within different thickness  normalized dermal sublayers  of the elastin network compared to the fibrillar collagen network. The 2PEF—elastin and SHG—collagen fibers masks are used to compute this parameter as the ratio between the intersection and union volumes of the 2PEF and SHG masks. A mean value is computed within the superficial dermis layer or normalized dermal sublayers. This parameter can have values between 0 and 1: a large value meaning that the signals are spatially correlated and a low value indicating that they tend to be mutually exclusive.To assess the multimodal 2PEF and SHG signals organization, we measure their overlapping, coincidence at the pixel level by a 3D quantification parameter called normalized imbrication (ImbrN)The 2PEF and SHG signal intensities can be estimated within the whole z-stack or within the different segmented volumes .For visualization, the combined 2PEF/SHG images in this paper and the 3D epidermal and dermal volume reconstructions were processed with Fiji/ImageJ . The 3D elastin fibers networks were reconstructed using Imaris .The raw data were described using boxplots with the dots and associated density curves displayed on the right side. For the analyses of skin constituent’s variability and robustness of the 3D multiphoton parameters, data from clinical study I with all 4 ROIs were considered. For the analyses of skin changes, based on the results of robustness, data from 2 ROIs per volunteer and per condition were retained for clinical study I. For clinical studies II and III, all available good quality z-stacks were considered . The boxplots were performed with OriginPro .For the analyses of skin constituent’s variability and robustness of the 3D multiphoton parameters, distributions of the parameters have been compared for 2 vs 3 ROIs and 2 vs 4 ROIs. The homogeneity of variances of the distribution have been compared using the Folded F test  where the F value is calculated as the ratio of the greater of the two variances divided by the lesser of the two variances. Means between ROI’s distributions were compared using a Student t-test.For the analyses of skin changes, the averaged values based on ~ 2 ROIs per volunteer and per condition have been taken into account for the following analyses.For clinical study I, comparisons between age and skin color ITA groups have been carried out using an Analysis of Variance (ANOVA) with age, skin color, and their interaction as fixed factors.For clinical studies II and III, comparisons between age and skin zones have been carried out using a Mixed model of variance analysis with age, skin zone, and their interaction as fixed factors, and subject as a random factor to take into account the within subject correlation between zones.For the analyses associated to aging difference in the z-dermal distribution, comparisons between age and dermal sub-layers by skin zone have been carried out using a Mixed model of variance analysis with age, dermal sub-layer and their interaction as fixed factors, and subject as a random factor to take into account the within subject correlation between dermal layers.p-value and allows meaningful comparison between different studies outcomes. The criteria for ES interpretation of multiphoton parameters have been built from contrasts clearly relevant in the study context42: ES very strong [1.3–Inf], ES strong [0.8–1.3], ES moderate [0.5–0.8], ES weak [0.3–0.5], ES very weak [0–0.3] and no effect if ES = 0.For all models, comparisons have been performed using contrasts with both t-tests and Cohen-D effect size (ES). Cohen-D is defined as the ratio of within group difference to the squared root of the variance of the sum of random parameters in the model. It gives an idea of the strength of the modifications observed between groups. The effect size depends only on the underlying population parameters, not on the sample size as the p-values have not been adjusted for multiplicity testing.Inference tests have been carried out using SAS 9.4 . The significance threshold for comparisons was set at 5% two-sided. The studies being exploratory, flex™—study III) in equivalent imaging conditions except for slight differences in the field of view (FoV), pixel dwell times and global systems’ sensitivity. In the following, we will firstly address the question of skin constituent’s intrinsic variability and robustness of 3D multiphoton parameters for skin assessment. Secondly, we will present the multiphoton multiparametric 3D quantification results of in vivo human skin changes with aging on forearm and face skin areas.To identify human skin changes with aging that could be non-invasively evidenced with multiphoton imaging, we performed three clinical trials see  on ventrNormal human skin has an intrinsic variability in its morphology and in the distribution, density and organization of its native constituents that can be visualized at different scales. Quantifying human skin with multiphoton imaging or in general with other methods implies combining the effects of both skin intrinsic variability and method repeatability .We first verified the multiphoton 3D imaging repeatability and confirmed the minimal influence of the imaging system itself on the skin multiphoton signals, by performing a live imaging experiment where 3D x,y,z-stacks were acquired every 10 min in the same imaging conditions and on the same region of the ventral forearm skin Fig. . One canIn general, in a clinical study different volunteers are included and for each volunteer and experimental condition one can measure one up to several regions of interest. In our studies, given the long image acquisition times (7–10 min/3D z-stack), we often acquire a maximum of 2 or 3 ROIs per volunteer per condition.To address the question of intrinsic skin variability impact on multiphoton parameters significance and evaluate their robustness for skin assessment, we performed a clinical trial on 20 young (30–40 year-old) and 20 aged (55–65 year-old) European origin volunteers (women and men) with “light” and “tan” skin forearm color (study I). We acquired a maximum of 4 ROIs per volunteer and quantified the 3D mean values of different multiphoton parameters.p-values being very far from the significant threshold , anatomical skin zone and aging in the three clinical trials Fig. . As expeThe melanin density z-distribution profiles allow refining the analysis. It highlights higher differences in the basal and supra-basal layers of “light” skin between the two groups of age in study I Fig. c1 and acAs shown in the multiphoton images in Fig. Anatomical site differences in the 3D elastin density were evidenced in young (18–25 year-old) volunteers between the dorsal forearm  and ventral forearm and face temple areas  and in middle age (30–40 and 55–65 year-old) volunteers between “light” and “tan” ITA groups. With aging, the most obvious changes were detected between the extreme age groups  and to a lesser extent into score 2 . In older volunteers, the elastin network can be qualified as a mixture of score 2 , score 3  and score 4 . To be noted, in young volunteers, score 1 is more frequent on face temple area compared to dorsal forearm, whereas in older volunteers, score 3 is more frequent on dorsal forearm and score 4 on face temple area, suggesting an initial difference in the elastin network, more stretched on the temple area than on the dorsal forearm, and a more severe elastosis on the more chronically exposed skin site with aging.This qualitative analysis Fig. c top allUsing the SHG signal of fibrillar collagens, we quantified the 3D mean density of collagen fibers within a 50-µm thick dermal sublayer that follows the shape of the DEJ Fig. b1–b3.As for elastin density, anatomical site differences in the 3D fibrillar collagen density were evidenced in young (18–25 year-old) volunteers between the dorsal forearm  and ventral forearm and temple areas   volunteers between “light” and “tan” ITA groups   on both forearm sides  and the more exposed (face) skin areas in study III, due to the high intrinsic fibrillar collagens densities on the face compared to dorsal forearm. Its aging increase is greater on the most exposed area . Despite this still relatively small investigated epidermal volume, we have previously shown that 3D morphological epidermal parameters (thickness and DEJ shape) as well as global 3D epidermal melanin density and its z-epidermal distribution32, enable studying various physiological conditions  or treatment effect . Here, we further demonstrated that for in vivo multiphoton human skin studies, our experimental protocol (2 ROIs per zone per volunteer) is robust compared to protocols involving 3 or 4 ROIs per volunteer and relevant in assessing in 3D normal human skin and its aging changes in terms of epidermal thickness, DEJ undulation, melanin density, elastin density, collagen density, SHGto2PEF densities ratio, SAAID density index and ImbrN normalized imbrication.Normal human skin has effectively a variability in its native constituents that can be evidenced at different scales from dermoscopy, histology to multiphoton and other higher resolution imaging techniques such as transmission electron microscopy. For example, on histological sections, one can clearly see the variations in epidermal thickness, simply because of the DEJ undulation, or the non-regular distribution of melanin in the basal layer. In dermis, variations between papillary and reticular dermis in terms of density of elastin (elastin fibers and elastosis) and collagen fibers, or their thickness and organization are another example. For the epidermis, compared to histology analysis, in which quantification is generally performed on a few 2D ROIs of ~ 200–250 µm epidermal length, in multiphoton 3D imaging the investigated skin volume is higher  and results were very similar. The modulation amplitude of some parameters slightly differs, first as they were not collected at the same time nor on the same volunteers, but also due to differences in the field of view, pixel dwell times and global systems’ sensitivity of the two microscopes. It is logical to consider that the larger FoV (MPTflex™) affords a more precise estimation of mainly the mean epidermal thickness, in cases where the DEJ is very undulated, as several dermal papillae can be imaged, depending on their size and shape, and the rete ridges detected. A larger (~ 1.6 × 1.6 mm2) skin area sampling was shown to improve the precision of melanin density estimation compared to smaller skin areas, without influencing its average value’ estimation44. Large-area based melanin quantification of different skin Fitzpatrick phototypes44 shown comparable results to our previous small-area constitutive pigmentation data32, albeit differences in melanin detection and experimental protocols. The small-area based 3D multiphoton quantification parameters , estimated within the plateau regions of the skin, may not afford absolute measurements but they have proven to be relevant in detecting normal human skin changes occurring physiologically42 or upon treatment application42. Considering the results in the current work, this statement can be extended to dermal 3D parameters and photo-aging characterization. For melanin assessment, a larger skin area would afford a more precise measurement, but in our opinion would mainly be of interest in assessing melanin heterogeneity in, for example, skin disorders and skin cancers. The influence of large-area sampling on epidermal morphology, dermal elastin and collagen fibers 3D parameters remains to be evaluated.In this work, the same age groups and, for part, the same skin area were investigated with two different CE-medically approved multiphoton systems 32. In addition to melanin amount quantification, its 3D epidermal distribution allows to objectify differences even when the global density does not vary. In study II, comparing aging (18–25 vs 70–75 year-old) differences between dorsal and ventral forearms, it evidenced a suprabasal accumulation both with age and with chronic exposure. Such modifications in melanin distribution were not evidenced in study III comparing same age groups and areas with different degree of exposure. Thus, this is to be explored in a future study with volunteers with homogeneous constitutive pigmentation and a quantified and comparable degree of sun exposure at inclusion, which was not the case in these two studies.Concerning pigmentation characterization, using the 3D melanin global density, we could non-invasively quantify the expected increase with skin color (“tan” > “light” ITA groups), in agreement with previous data on constitutive and acquired pigmentationstratum corneum, a thicker living epidermis and a higher elastin and fibrillar collagens densities on the face temple area compared to the dorsal forearm, both in young and older volunteers, corresponding to intrinsic anatomical differences already reported in histological studies46. A thinner SC was also reported with confocal reflectance and Raman measurements on cheek (28–54 year-old)47. The great interest of the non-invasiveness of in vivo multiphoton imaging is really to be emphasized particularly when concerning the face. Photoaging of the temple area evolves as a function of chronic exposure as measured by the more pronounced increase in elastin density and the slight increase in fibrillar collagens density, the decrease in the 3D SHGto2PEF ratio and 3D SAAID index and the increase in the normalized imbrication 3D ImbrN index. For the area comparisons, due to the intrinsic higher elastin and fibrillar collagens amount on the face and to their aging increase, it is the normalized imbrication 3D ImbrN parameter that allows to refine the share of photoaging in global aging, whereas 3D SAAID do it the best on the forearm. The normalized imbrication parameter 3D ImbrN, measuring the degree of spatial correlation between these two fiber networks, gives an insight into their mutual organization and translates their entanglement. In the context of aging, the modulation of these three dermal parameters is mainly driven by the important modification of elastin density and their interest in other clinical contexts  remains to be determined.Regarding studied areas, the major differences evidenced are a thinner The study of two groups with different skin color  draws attention to the necessary precautions when interpreting the quantification of dermal fibrillar networks in this context. Indeed, we clearly observe a lower fibrillar collagens density in the more pigmented group in both age groups. We cannot totally exclude a difference linked to different phototypes, as elastin density was also smaller in the “tan” ITA group, but the subjects are all of European origin and such a difference is surprising and is not in accordance with histological knowledge. Moreover, although to a lesser degree, the same observation can be made between the more pigmented dorsal side and the ventral side of the forearm within the same group (study II). In our opinion, the most likely explanation is an attenuation of the dermis SHG signal by both absorption and scattering phenomena in thicker SC and epidermal areas with increased melanin density, as suggested by the negative correlations. Therefore, studies comparing different groups of skin color should be limited to epidermis, melanin and elastin characterization and comparison of SHG dermal signals restricted within the same phototype or carefully interpreted between anatomical areas with too strong pigmentation differences.In conclusion, in this work (1) we introduced a multiphoton multiparametric quantification toolbox for 3D skin assessment, (2) studied their robustness in characterizing normal human skin pigmentation and aging changes with respect to skin intrinsic variability and (3) applied these tools to skin aging characterization on different UV exposed areas—ventral and dorsal forearms, face. The three major facts of aging that are epidermal atrophy, the dermal–epidermal junction flattening and dermal elastosis can be non-invasively quantified and compared. Epidermal morphological changes occur late and were only objectified between extreme age groups. Melanin accumulation in suprabasal layers with age and chronic exposure on ventral and dorsal forearms is less known and appears earlier. Superficial dermal aging changes are mainly elastin density increase, with no obvious change in collagen density, reflected by SHGto2PEF ratio and SAAID index decrease and ImbrN index increase on all skin areas. Analysis of the z-dermal distribution of these parameters highlighted the 2nd 20 µm thickness normalized dermal sub-layer, that follows the DEJ shape, as exhibiting the highest aging differences. Moreover, the 3D ImbrN index allows refining the share of photoaging in global aging on face and the 3D SAAID index on forearm, which elastin or fibrillar collagens densities alone do not allow. Photoaging of the temple area evolves as a function of chronic exposure with a more pronounced increase in elastin density, also structurally modified from thin and straight elastic fibers in young volunteers to dense and compact pattern in older ones.49 or the fibers orientation17 estimated up to now only in specific 2D planes. Other organization12 parameters could also be implemented to address the fibers networks aging changes in 3D and will be the subject of another paper. Besides skin aging applications, we have previously shown the potential of 3D quantification tools to non-invasively characterize skin constitutive pigmentation32 and assess the effects of anti-aging compounds such as retinoic acid and retinol42. More generally, multiphoton multiparametric 3D skin quantification offers rich spatial information of interest in assessing normal human skin condition and its pathological, external environment or product induced changes.This work also highlights the importance of using different 3D morphology, density and organization parameters to create a global picture of not only the dermal fibers but also of the epidermis morphology and its melanin pigment. This analysis could be completed by other measurements such as the size and number of cells within living epidermisSupplementary Information.


The prevalence of obstructive sleep apnea is higher in women after menopause. This is suggested to be a result of an altered sex hormone balance but has so far not been confirmed in a population-based study.To investigate whether serum concentration of estrogens and progesterone are associated with the prevalence of sleep apnea symptoms in middle-aged women of the general population.We analyzed data from 774 women (40–67 years) from 15 study centers in seven countries participating in the second follow-up of the European Community Respiratory Health Survey (2010–2012). Multiple logistic regression models were fitted with self-reported symptoms of sleep apnea as outcomes and serum concentrations of various estrogens and progesterone as predictors. All analyses were adjusted for relevant covariates including age, BMI, education, study center, smoking habits, and reproductive age.Among all included women, a doubling of serum concentrations of estrone and progesterone was associated with 19% respectively 9% decreased odds of snoring. Among snorers, a doubling of the concentrations of 17β-estradiol, estrone and estrone 3-sulfate was associated with 18%, 23% and 17% decreased odds of breathing irregularly, and a doubling of the progesterone concentration was further associated with 12% decreased odds of waking up suddenly with a chocking sensation. Other evaluated associations were not statistically significant.Middle-aged women with low serum estrogen and progesterone levels are more likely to snore and report symptoms of obstructive sleep apnea. Obstructive sleep apnea (OSA) is characterized by repeated cessations of breathing, caused by upper airway obstruction ,2. ApartAlthough all these observations are suggestive for a potential impact of female sex hormones on the risk of OSA, to date there is no population-based or large clinical study available; only a handful of smaller studies have reported lower levels of endogenous female sex hormones –19 in woWith the aging of the general population the time women expect to live after menopause is becoming an ever-increasing portion of their total lifespan. Considering that OSA may lead to cardiovascular conditions (ischemic heart disease and stroke) , which aThe analyses were based on 774 complete case observations from the second follow-up (2010–2012) of the European Community Respiratory Health Survey . Examinations consisted of a structured interviewer-led questionnaire on respiratory health and lifestyle factors; a validated sleep questionnaire  and an iEthical approval was obtained from the appropriate ethical committee of each study center and all participants provided informed written consent before being enrolled in the study. The specific committees were for Aarhus: Scientific ethical committee for Region Midtlylland; Albacete: Comité Ético de Investigación Clínica del Hospital Universitario de Albacete; Galdakao: Comité Ético de Investigación Clínica del Hospital de Galdakao-Usansolo; Huelva: Comisión de Ética de Investigación Sanitarias del Hospital Juan Ramón Jiménez de Huelva; Bergen: Regional Committee for Medical and Health Research Ethics in Western Norway (2010/759); Bordeaux, Grenoble, Montpellier, Paris: Comite De Protection Des Personnes (2011-A00013-38); Erfurt, Hamburg: Ethikkommission der Bayerischen Landesärztekammer (Reg Nr. 10015); Uppsala, Umeå, Göteborg: Ethics Committee of Uppsala University (2010/068); Reykjavik: National Bioethics Committee of Iceland (VSN 11–121); Tartu: Research Ethics Committee of the University of Tartu (209/T-17);The serum samples of the participants were analyzed at the Core Facility for Metabolomics at the University of Bergen (Norway) for concentrations of the steroid hormones 17β-estradiol, estrone, estrone 3-sulfate and progesterone, using a high-sensitivity liquid chromatography-tandem mass spectrometry method . The meaIn the sleep questionnaire women were asked “Have you been told that you snore”, which we used as main outcome (1. Snoring). An affirmative answer lead to three subsequent questions: “In the last 12 months have you been told that you stop breathing or have irregular breathing while you are sleeping?” (2. Irregular breathing); “Have you woken up all of a sudden with a choking sensation or not being able to breathe in the last 12 months?” (3. Gasping); “Have you been told that you snore loudly or that your snoring disturbs other people in the last 12 months?” (4. Disturbing snore). The response options to these questions were: “never”, “seldom”, “sometimes”, “frequently” or “every time”. If women answered “no” to the question “Have you been told that you snore?” they were allotted the answer “never” to all three subsequent questions for the analysis. In order to transform the outcome into a dichotomous variable we merged all affirmative answers .2-transformed) as predictors (one at a time) and snoring, irregular breathing, gasping and sleep disturbance as outcomes (one at a time). We adjusted the analysis for a predetermined set of covariates: age (continuous), BMI (continuous), smoking habits , age at completed full time education  as a socioeconomic proxy, study center and a novel reproductive score (RAS) based on fuzzy logic, which describes the progress of reproductive aging in a continuous manner, taking into account menstrual frequency and age  for measured hormone concentrations.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.


The aim of this study was to investigate whether it is possible to use infrared thermography to assess cardiovascular fitness and aerobic capacity. Changes in temporal temperature during and after a single bout of high-intensity exercise were measured from subjects with varying levels of physical activity. Significant correlation between the temporal temperature measured during recovery time with cardiovascular fitness parameters (HRR and HRV) and maximum oxygen consumption confirm the usefulness of thermal imagining in aerobic capacity evaluation. These results could foster the employment of infrared thermography to monitor athletic/athletes’ performance.2max: 59.03 ± 11.19 (mL/kg/min), body mass 71.5 ± 10.4 (kg), body height 179 ± 8 (cm)) participated in the study. Each subject performed an incremental test and, after a 48-h interval, a 110%Pmax power test combined with an analysis of the thermal parameters, heart rate recovery and heart rate variability. Thermal radiation density from the body surface (temple) was measured using a Sonel KT384 thermal imaging camera immediately after warm-up (Tb), immediately after exercise (Te) and 120 sec after the end of exercise (Tr). The differences between measurements were then calculated. The correlation analysis between the thermal and cardiovascular function parameters during the recovery period showed strong positive associations between the Tr-Te difference and measures of cardiovascular fitness . For example, the correlation coefficient between Tr-Te and VO2max reached 0.55 and between Tr-Te and Pmax reached 0.68. The results obtained indicate that the measurement of temporal temperature during and after an intense 3-min bout of exercise can be used to assess aerobic physical capacity and cardiovascular fitness.The aim of this study was to determine whether there are associations between cardiovascular fitness (and aerobic capacity) and changes in temporal skin temperature during and after a single bout of high-intensity exercise. Twenty-three men with varying levels of physical activity (VO RepeateThe aim of this study was to determine whether there are associations between cardiovascular fitness (and aerobic capacity) and changes in temporal skin temperature during and after a single bout of high-intensity exercise. It was assumed that, among other things, the rate of maximal oxygen uptake (as well as the rate of heart rate recovery) would positively correlate with the change in temporal temperature post-exercise.A group of 23 men with varying levels of physical activity participated in the study. The participants were free of any known neuromusculoskeletal, cardiovascular and respiratory systems impairment. A total of 6 participants led a sedentary lifestyle, 10 were classified as a physically active  and 7 were classified as athletes . The study was approved by the University Ethics Committee and conducted in accordance with the ethical standards established by the Declaration of Helsinki. Participants were made aware of the experimental protocol and gave written consent to participate prior to the study.The subjects had not performed heavy physical exercise in the 48 h prior to the exercise tests. Each subject performed an incremental test for cardiovascular fitness and aerobic efficiency assessment as well to determine the power of the verification test equal to 110% of Pmax obtained in GXT. After a 48-h interval, a 110%Pmax power test combined with an analysis of the thermal parameters, heart rate recovery and heart rate variability were carried out .biceps brachii of the dominant hand to estimate the body fat expressed as kilograms and percentages of body weight, lean body mass (LBM) and body water content in liters and as a percentage of the body mass. It is a commonly used method for body composition measurements [Before the test, body composition was measured using a near-infrared device NIR . The device measures the optical density of body tissues on the urements ,31.A graded exercise test (GXT) was performed to determine aerobic power ,32. The 2—5%, O2—16% and N2—79%. The respiratory parameters (oxygen uptake (VO2), exhaled carbon dioxide (VCO2), and minute pulmonary ventilation (VE)) were measured. The analysis of the data was carried out with the results averaged every 30 s. The highest VO2 recorded in GXT defines VO2peak1. Heart rate (HR) was recorded with the V800 cardiofrequencimeter .During testing, subjects breathed through a mask as their expired air was sampled breath by breath and analysed by a Quark CPET gas analyser . The apparatus was calibrated with atmospheric air and a gas mixture composed of the following elements: CO2−1 equivalent without a concomitant increase in VE·VCO2−1, was determined from the recorded respiratory data from the GXT test; the second ventilation threshold (VT2) was determined at the point preceding the second non-linear increase in VE·VO2−1 or VE·VCO2−1 equivalent, following the methodology described by Beaver et al. (1986) [The first ventilatory threshold (VT1), at the point before the first non-linear increase in VE·VO. (1986)  and Davi. (1986) .2peak2). Maximal oxygen uptake (VO2max) was considered to be the higher than VO2peak1 and VO2peak2, as in earlier work [2peak1, VO2peak2 and VO2max) and lean body mass . Each exercise test was performed under controlled thermal conditions. The air temperature was 21 °C and the humidity was between 40–45%.A Lode Excalibur Sport cycloergometer and a Quark ergospirometer were used to perform the test. The test was preceded by a 15-min warm-up, consisting of exercising for 5 min at an intensity corresponding to the power achieved at the VT1, followed by 10 min at a power corresponding to halfway between the VT1 and the VT2. The warm-up was followed by a 10-min passive break. The verification test lasted 3 min and was performed at an intensity of 110% of the Pmax achieved in the GXT test performed two days earlier. Recording of the respiratory parameters began 1 min before the verification test and ended 5 min after the test. The analysis of the data was carried out using the results averaged every 30 s. The highest recorded oxygen uptake value (from 30 sec averaging) was considered to be the peak oxygen uptake in a verification test performed on a separate day (VOier work . The powb), immediately before the start of the exercise at 110%Pmax. Further temperature determinations were made immediately after exercise (Te), and 120 s after exercise (Tr). The difference between Te and Tb (T1 = Te − Tb), the difference between Tr and Tb (T2 = Tr − Tb) and the difference between Tr and Te (T3 = Tr − Te) were then calculated.During the test at power 110%Pmax, the density of thermal radiation from the body surface was measured using a thermal imaging camera Sonel KT384 . The thermal imaging camera was used in accordance with the manufacturer’s guidelines. The camera features IR resolution of 384 × 288; spectral range of 8–14 μm; thermal sensitivity of 0.08 °C. The software provided by the manufacturer  converted radiation density into body surface temperature expressed in °C. During playback of the recorded video, individual frames were analysed and the mean temperature was recorded within a square box (with a side length of 10 pixels), individually marked on the temple , as in awww.flow.polar.com accessed on 22 October 2021) in a precisely marked part of the saved data. The changes in heart rate during recovery after warm-up were then calculated as the difference between the heart rate measured at the end of warm-up and HRR1’ (ΔHRR1’), HHR2’ (ΔHRR2’), HRR3’ (ΔHRR3’), HRR4 (ΔHRR4’) and HRR5’ (ΔHRR5’), respectively, similar to Suzic Lazic et al. (2017) [During the warm-up and a 10-min passive break before the 110%Pmax power test, the time interval between heartbeats (RR) was recorded using a V800 cardiofrequency meter . Heart rate values were averaged for 59–61” (HRR1’), 119–121” (HRR2’), 179–181” (HRR3’), 239–241” (HRR4’) and 299–301” (HRR5’) recovery after warm-up, as recovery RR interval measurements have a high variability. The RR intervals allowed for the analysis of consciously selected sections for HRR analysis. A simpler method, such as HR recording, would result in a random entry segment for the HRR analysis, as the cardiofrequencymeter’s software calculates the HR based on the averaging data at 3 s intervals. The conversion of RR to HR was performed automatically by PolarFlow (3–5′) and standard deviation of normal-to-normal RR intervals (SDNN3–5′) were calculated from the data recorded by the cardiofrequencimeter during recovery after the warm-up. For the frequency domain, a spectral analysis was performed using fast Fourier transformation to obtain low-frequency spectral power (LFP3–5′) and high-frequency spectral power (HFP3–5′). For this purpose, the Kubios HRV Standard software  was used. HRV parameters were calculated for the portion of the recording covering the third, fourth and fifth minutes of recovery, similar to the methodology previously used by Buchheit et al. (2009) [For the temporal HRV parameters, the square-root of the mean squared difference between successive normal-to-normal RR intervals (RMSSD. (2009) . Medium For the analyses of HRV, HRR and ΔHRR, we used data collected in post-warm-up recovery as these data allow analyses to be performed after standardised moderate-intensity exercise, similar to the procedures used by other authors ,37.The Shapiro–Wilk test was used to assess the distribution of the parameters studied. Analysis of variance with repeated measurements was used to compare the parameters that were measured several times during the study. Pearson’s simple correlation was used to determine the strength of the relationships between the changes in temporal temperature and measurements of cardiovascular fitness . The scale modified by Hopkins et al. (2009)  was usedr > 0.5. The following formula was used:Using the formula for the critical value of the correlation coefficient, the minimum group size was calculated assuming that the acceptable level of statistical significance  is 0.05 and that the strength of the correlation should be high, t ≈ 2.15.On this basis, we determined that the minimum number of study participants was 16 at b,e,r . Using a post-hoc test, significant differences were shown between Tempe and Tempb and between Tempr and Tempe  and strong relationships of T3 with Pmax, Pmax·LBM−1, VO2peak1, VO2peak2, VO2max, VO2max·LBM−1  and ΔHRR . Using post-hoc tests, it was shown that HRR and ΔHRR measurements performed in the first minute of recovery were significantly different from those performed in the second, third, fourth and fifth minutes of recovery , statistically significant relationships of T1 with ΔHRR2’, ΔHRR3’ and T3 with HRR1’, HRR2’ HRR4’, HRR5’, ΔHRR1’ ΔHRR2’ ΔHRR4’ ΔHRR5’ were demonstrated , statistically significant associations (p < 0.05) of T2 with SDNN1 and strong (0.50 < r < 0.69) of T3 with all HRV parameters analysed in recovery were demonstrated   . The dire effort .2max . However, the exercise test protocol used in previous studies [2max [The relationship between T3 and VO2max  that we  studies  is not ces [2max ,59,60,61es [2max . For thi2) between T3 and recovery HRV parameters reached 45% in our analyses (T3 and RMSSD3–5′).In the current study, HRV recovery and ΔHRR were measured after a moderate-intensity warm-up, and T3 was measured during the high-intensity exercise that followed the warm-up. Nevertheless, our results show strong correlations of T3 with ΔHRR and with HRV  paramete2max level is very strong [2max should allow a correlation of T3 with Pmax to exist and the current results shows this correlation, which was strongest (r = 0.68) when Pmax was expressed as Pmax·LBM−1  were positively correlated with the measurements of cardiovascular fitness applied. It is also possible to assess the level of aerobic capacity (ability to perform intense aerobic work) on the basis of recovery changes in temporal temperature. Furthermore, exercise-induced changes in temporal temperature correlated with the measurements of cardiovascular fitness, although in this case, the relationship was negative. The results obtained indicate that the measurements of temporal temperature during and after an intense 3-min bout of exercise can be used to assess aerobic physical capacity and cardiovascular fitness.


Continuous Positive Airway Pressure (CPAP) is the gold standard treatment for OSA. A wide variety of masks are used during CPAP therapy. The aim of the study is to compare polysomnography outcomes during titration for different types of masks  and assess the impact on PAP titration.We retrospectively analyzed all CPAP titration polysomnography for one year. Anthropometric data and baseline apnea severity were documented.2O for the oronasal mask, 10.1 ± 2.1 cm H2O for the nasal mask and 9.8 ± 2.2 cm H2O for the pillow. The residual apnea-hypopnea index was 10.4 ± 7.9 for the oronasal mask, 5.49 ± 5.34 events/h for the nasal mask and 4.98 ± 5.48 events/h for the pillow. The baseline apnea-hypopnea index was correlated with of a higher CPAP pressure for all the groups . Body mass index and residual AHI were correlated with of a higher CPAP pressure for the nasal and pillow groups only (p < 0.001).A total of 497 patients were evaluated. Nasal masks were used in 82.3% (n = 409), pillow in 14.1% (n = 70) and oronasal in 3.6% (n = 18). There was no difference according to body mass index and baseline apnea-hypopnea index among the groups. The oronasal group was older and had higher titrated CPAP pressure, higher residual apnea-hypopnea index, lower sleep efficiency, more superficial N1 sleep and longer wake time after sleep onset. All other polysomnography outcomes were similar among the groups. The mean CPAP level was 11.6 ± 2.1 cm HThe interface can have a significant impact on the effectiveness of PAP titration. Patients with oronasal masks have higher CPAP pressure, higher residual apnea-hypopnea index, lower sleep efficiency and higher wake time after sleep onset. Oronasal masks should not be recommended as the first choice for apnea patients.: Level 3. Obstructive Sleep Apnea (OSA) is characterized by repeated episodes of obstructive respiratory events during sleep. The airflow limitation results in oxygen desaturation and sleep fragmentation. Untreated OSA is associated with significant morbidity and mortality, with increased risk of hypertension, cardiac arrhythmias, depression, motor vehicle accidents and premature death.2Patients with OSA are treated with Continuous Positive Airway Pressure (CPAP), which is delivered during sleep, and it is considered the gold standard treatment from moderate to severe cases. The level of pressure can be titrated through polysomnography when delivering CPAP in a sleep laboratory, where the patient is seen by a technician who is an expert in polysomnography and who will choose the type of mask to be used with CPAP. There are two main types of masks: those placed over the nose and those placed over the nose and the mouth at the same time. There are two nasal mask styles: nasal itself and nasal pillow. Nasal masks cover exclusively the nose while not compressing the nose wings by being placed immediately above the upper lip and near the eye angle. Nasal pillow masks are two intranasal cushions that have emerged as an alternative to the nasal mask for being smaller and having less contact with the face. The oronasal mask covers the nose and the mouth, and it enables the patient to breathe through the nose and the mouth.The choice of CPAP delivery interface usually occurs based on the clinician’s personal experience or patient preference.6A few studies have looked at compliance with different types of PAP masks.This study compared oronasal, nasal and nasal pillow masks during CPAP titration night. We studied the polysomnography outcomes during CPAP titration and assessed whether patient’s characteristics (anthropometric data) differed among the mask preference groups. The aim of the study is to compare polysomnography outcomes during titration for different types of masks  and assess the impact on the effectiveness of PAP titration.We retrospectively studied all the patients with OSA who have consecutively submitted to CPAP titration for one year, from May 2017 to April 2018, during our clinical practice at the sleep center of our institution.The following data were recorded for all the patients: age, sex, Body Mass Index (BMI) and baseline Apnea-Hypopnea Index (AHI). The inclusion criteria were age > 18 years old, diagnosed with OSA who underwent polysomnography for CPAP titration. The exclusion criteria were patients under 18 years old; patients who required bi-level positive airway pressure (Bilevel); submitted to automatic titration; submitted to split-night study, patients who switched masks during the titration; patients without baseline AHI; patients with central sleep apnea; patients with severe pulmonary disease (PCD or hypoventilation); patients with severe heart disease (CHF) and patients with dementia; with no information by technician regarding the mask style; and by technical mistake.All the patients underwent full-night CPAP titration with standard polysomnography according to the American Academy of Sleep Medicine standards. Multi-channel recordings of the electroencephalogram , Electrooculogram (EOG), Electromyogram (EMG), oronasal flow (by mask), respiratory effort , oxygen saturation (pulse oximetry), snoring and body position were recorded on a computerized workstation .Effective pressure was attained when evidence of apneas, hypopneas and snoring were ameliorated according to AASM guidelinesA sleep technician manually titrated pressure. All patients used the Philips-Respironics REMstar device. Heated humidification was provided for all the patients in the study. All the titration studies were reviewed by a single board-certified sleep specialist and the final prescribed CPAP level for each study was determined. Effective pressure was attained when evidence of apneas, hypopneas and snoring were ameliorated.Education and mask fitting were performed preceding the titration study by the attending technician. For all the types of interfaces, adjustments were made until the patient felt comfortable and a proper fit was obtained. Nasal masks were fitted using the sizing gauge, while nasal pillows were fitted using side straps. The size of the nasal pillow was reassessed after placement and changed if needed based on adequate seal. Patients were placed on CPAP for a few minutes while awake and could switch to another type of mask. Additional adjustments were made as needed during the laboratory titration to minimize leak and maximize comfort. The recommendation was to start off with a nasal or nasal pillow mask, while switching to an oronasal mask only if the patient was unable to breathe nasally or had substantial mouth leak, after the use of chin straps during the titration to prevent mouth leak. Oronasal masks were used only if the patient had great difficulty breathing through the nose and/or was unable to keep their mouth closed.The trial was approved by the Ethical Review Board of our institution (CEP:2.676.587). The study was not supported by any sponsor.p < 0.05 values indicated statistical significance. Data were analyzed with the use of Stata/SE v.14.1. StataCorpLP, USA computer program.The results of the quantitative variables were described by means, standard deviations, medians, minimum and maximum values. For the categorical variables, frequencies and percentages were presented. To compare the three groups defined by the mask type against the baseline AHI variable, the one-way analysis of variance (ANOVA) was used. Comparisons were made using the covariance analysis model (ANCOVA) for other quantitative variables, while including baseline AHI as a covariate. To assess the correlation between quantitative variables and CPAP pressure, Pearson linear correlation coefficients were estimated. Data on quantitative variables that did not meet the normal condition were submitted to a logarithmic transformation; Data were evaluated from 698 consecutive patients for whom CPAP therapy was recommended. According to the exclusion criteria, 201 patients were not included in the analysis: patients under 18 years old (n = 05); with no information regarding the mask style (n = 01); patients who required bi-level positive airway pressure (BiPAP) (n = 26); submitted to split-night study (n = 76); patients with central sleep apnea (n = 08); patients with pulmonary diseases (n = 05); patients without baseline AHI (n = 69); and technical mistake (n = 11). Finally, data from 497 patients were analyzed. Nasal masks were used in 82.3% (n = 409), nasal pillow masks in 14.1% (n = 70) and oronasal masks in 3.6% (n = 18).p = 0.008). There was no difference between the groups regarding body mass index.The characteristics of the study groups are shown in 2O for the oronasal mask; 10.1 ± 2.1 cm H2O for the nasal mask and 9.8 ± 2.2 cm H2O for the nasal pillow. The residual apnea-hypopnea index  was 10.4 ± 7.9 events/h for oronasal mask; 5.49 ± 5.34 events/h for nasal mask and 4.98 ± 5.48 events/h for nasal pillow.The titration polysomnography data are shown on p < 0.001).The baseline apnea-hypopnea index was positively correlated with CPAP pressure for all the groups . Body maOronasal mask appeared with the intention of attending patients with oronasal breathing and represents 25% to 45% of the masks used according some studies.Even though oronasal masks are frequently used by sleep laboratories during CPAP titration night, in our results only 3.6% of the patients studied used oronasal masks, which shows that the oronasal mask was an exception choice in our data. This could be related to the fact that technicians in our sleep laboratory are trained and guided to perform selective and careful acclimation, while encouraging patients to use the nasal/nasal pillow mask. Even patients with mouth breathing or nasal obstruction history were encouraged by technicians to try and test the nasal/nasal pillow mask; only patients who did not feel comfortable while using the nasal/nasal pillow mask were then tested with the oronasal.Our result shows that once the patient is encouraged to use the nasal or nasal pillow mask, most of them continue to use that style during titration night. Our data also show that most patients who used oronasal masks were male and older (average of 62 years old). According to other studies, when given the option to choose from, most patients preferred the nasal mask to the oronasal one.2O, on average), a signiﬁcantly higher residual AHI (+2.8 events/h) and lower adherence (−48 min/night).2O for the oronasal mask; 10.1 ± 2.1 cm H2O for the nasal mask and 9.8 ± 2.2 cm H2O for the nasal pillow. Residual AHI was 10.4 ± 7.9 events/h for the oronasal mask; 5.49 ± 5.34 events/h for the nasal mask and 4.98 ± 5.48 events/h for the nasal pillow.According to our data, the oronasal mask performed worse in the titration polysomnography when compared to the nasal and nasal pillow masks in the following aspects: titrated CPAP pressure was higher, sleep efficiency was lower, patients were awake for longer, patients had more superficial non-REM N1 sleep, and residual AHI was higher. According to studies and a meta-analysis that compared the impact of nasal and oronasal CPAP, the treatment of OSA with oronasal masks is associated with a signiﬁcantly higher CPAP level  with an oronasal mask.Another study that reported 4 cases of patients who underwent CPAP titration also showed differences among those patients, while considering the type of mask used.The change to nasal mask significantly reduced the residual AHI (3.1 ± 1.5 events/h). It was shown that in two out of the four cases, the control of respiratory events was possible with a much lower level of CPAP pressure compared to oronasal masks. These authors suggest that for patients that use CPAP with oronasal masks and have residual apnea and/or need a surprisingly high CPAP pressure level, the choice of the mask should be reevaluated and the change to a nasal mask should be considered.In our experience, oronasal masks represent a low percentage of interface used during CPAP titration. Patients with oronasal masks have higher titrated CPAP pressure, higher residual AHI, lower sleep efficiency, more superficial N1 sleep and higher WASO during titration. The use of nasal or nasal pillow masks should be encouraged by sleep laboratories as the first choice on the CPAP titration night.Adriane Iurck Zonato and Cíntia Felicio Adriano Rosa participated in data collection and manuscript writing; Luciana Oliveira and Lia Bittencourt participated in critical manuscript revision. All authors had substantial contributions to drafting the article or revising it critically for important intellectual content and approved the final version of this article.Ethics approvalAll procedures performed in studies involving human participants were following the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was a retrospective trial and was approved by the Ethical Review Board of our institution.All authors have no actual or potential conflicts of interest to disclose, including financial interests and relationships.


Our experimental and theoretical results give evidence that oleic acid adsorbs dissociatively on both facets at lower coverages. At higher coverages, the more pronounced molecular adsorption causes hydrogen bond formation between the carboxylic groups, leading to a more upright orientation of the molecules on the (111) facet in conjunction with the formation of a denser layer, as compared to the (001) facet. This is evidenced by the C=O double bond infrared line shape, in depth molecular dynamics bond angle orientation and hydrogen bond analysis, as well as X-ray reflectivity layer electron density profile determination. Such a higher density can explain the higher mechanical strength of nanocomposite materials based on magnetite nanoparticles with larger (111) facets.The microscopic understanding of the atomic structure and interaction at carboxylic acid/oxide interfaces is an important step towards tailoring the mechanical properties of nanocomposite materials assembled from metal oxide nanoparticles functionalized by organic molecules. We have studied the adsorption of oleic acid  and (111) surfaces experimentally by SXRD, LEED, FT-IRRAS and XRR, and employ a molecular dynamics force field specifically developed for magnetite/organic interfaces to determine molecular adsorption geometries, coverages, layer thicknesses and adsorption-induced structural changes in the substrate. The interaction of organic molecules with the oxide nanoparticle surfaces and the interlinking of the molecules is argued to be the key element for their exceptional mechanical stability. Moreover, magnetite is an efficient material for the removal of glyphosate from water4. Employed in biomedical imaging or as drug carriers in medicine5, iron oxide nanoparticles also have a promising potential for theranostatic applications, e.g. magnetic hyperthermia6. In order for targeted drug delivery or theranostic tissue engineering, nanoparticles must, however, be functionalized. Monodisperse magnetite nanoparticles starting from a few nanometers in size up to 50 nm or more can be formed into various shapes ranging from cubic to truncated cuboctahedrons11. A common synthesis route for magnetite nanoparticles is precipitation from a precursor in a bath of boiling oleic acid (OLAC), leading to monodisperse nanoparticles with defined, OLAC covered facets12. OLAC is a preferred organic ligand for many applications because it can bind strongly to the oxide surface, but it also allows the easy customization of the functionalization by ligand exchange2.Metal oxide nanoparticles (NPs) functionalized by organic molecules are important building blocks for novel nanocomposites in materials science with exceptional bending modulus, hardness, and mechanical strength1. The molecular binding geometry, orientation and density, as well as the near-surface structure and composition of the oxide nanoparticles influence the nanoparticle growth, binding, interface structure, and ultimately their performance in the above mentioned applications. Using Fourier transform infrared reflection-absorption spectroscopy (FT-IRRAS), OLAC was found to dissociate on the nanoparticle surface1 and adapting chelating adsorption geometries13 , or bidentate bridging  together with non-dissociated molecules14, or both mono- and bidentate geometries15. However, the connection between binding mode and magnetite surface orientation is still missing. It has also been experimentally observed that the formation of the rod-like morphology of magnetite nanoparticles is a result of the growth along their (110) direction which was attributed to the preferential adsorption of oleate on the (111) facets of Fe3O416. A microscopic explanation of this observation is again lacking.During synthesis OLAC stabilized magnetite nanoparticles mainly form (001)-type and (111)-type facets, but not much is known about the ligand arrangement on different surfaces and ligand adsorption-induced faceting mechanisms218. The magnetite (001) surface presents a 20. The reconstruction of this oxygen rich surface, consisting of two subsurface octahedral (oct) vacancies and one additional tetrahedral (tet) iron ion, is lifted upon adsorption of atomic hydrogen, water21, or formic acid18 by iron cation rearrangement at room temperature, reestablishing a stoichiometric surface.Formic acid, the simplest carboxylic acid, is an important prototypical molecule for the adsorption of larger carboxylic acids like OLAC, and exhibits the same carboxylic end group which binds to the oxide surface. On the magnetite (001) surface, formic acid adsorbs in a bidentate bridging geometry at room temperature with a saturation coverage of 2.8 molecules/nm22 altering the adsorption behavior. The magnetite (111) single crystal surface is terminated by tetrahedrally coordinated iron ions (layer notation Fetet1) over a closed packed oxygen layer after (reducing) UHV preparation. The Fetet1 layer presents around 20% point defects (Fetet vacancies) making oxygen-terminated areas accessible23. Formic acid adsorbs on this surface in two different geometries: chelating and quasi-bidentate i.e., one carboxylate oxygen atom bound to a Fetet1 ion and the other bound to an OH group on the surface. The saturation coverage of formic acid is higher than on magnetite (001) and reaches up to 3.3 molecules/nm2.The surface termination of magnetite (111) is preparation-dependent and multiple terminations can coexist1. Although, it should be noted, that we employ specific conditions to ensure clean oxide surfaces rather than the usual thermal decomposition reaction found in nanoparticle synthesis12. While an ab initio-based description of the interface between OLAC and magnetite would be advantageous due to the reactivity of OLAC, the vast configurational freedom and intricate adsorption structures that OLAC exhibits on both surfaces prevent the use of such approaches, especially when studying high surface coverages. However, a properly parameterized force field provides an effective and accurate remedial strategy.Understanding the carboxylic acid/oxide interface starting from adsorption on planar (001)- and (111)-type facets of magnetite, the most prominent crystal orientations on magnetite NPs, is of utmost importance for many fields in materials science, such as the development of new nanocomposites with tailored mechanical properties. In the present work, we studied the adsorption of OLAC on magnetite single-crystalline (001) and (111) facets under UHV conditions focussing on adsorption coverage and binding geometries as well as the structural changes in the substrate using complementary FT-IRRAS, low energy electron diffraction (LEED), X-ray reflectivity (XRR), surface X-ray diffraction (SXRD) and molecular dynamics (MD) simulations. Investigating the adsorption under (reducing) UHV conditions is comparable with the conditions used to produce magnetite nanoparticles from a FeO(OH) precursor24, allow a rigorous determination of the interfacial properties and, by comparison with experimental results, illustrate the transferability of our empirical force field to complex carboxylic acids on magnetite surfaces.Our MD simulations, optimized specifically for the accurate description of dissociative adsorption of carboxylic acids−6 mbar and room temperature. The LEED patterns of the (001) surface before and after OLAC adsorption are shown in the inset of Fig. 19. After OLAC adsorption, the superstructure spots of the SCV reconstruction disappeared, indicating a lifting of the reconstruction, which was also observed for formic acid adsorption on the magnetite (001) surface18. The LEED pattern was also attenuated and got diffuse, which indicates that the adsorbed OLAC molecules are disordered without any preferential orientation of their aliphatic tail.The structural response of the magnetite 001) and (111) surfaces upon OLAC adsorption was first investigated by LEED and SXRD, see Figs.  and  signal at K = 220. The surface reconstruction signal vanished after OLAC adsorption and the CTR signal was enhanced, characteristic for reconstruction lifting and a strong near-surface structural rearrangement and cation diffusion from the bulk at room temperature, as it was also observed after formic acid17 or atomic hydrogen21 adsorption. The results of the quantitative analysis of the SXRD data  in the topmost iron layer show a smaller relaxation (−0.011 Å) compared to the clean surface (−0.296 Å). This is expected since there is an adsorbate on top and it is also consistent with the adsorption of formic acid on the magnetite (001) surface17. Displacement (and occupancy) error bars are given in Supplementary Discussion z) towards the octahedral iron ions which is consistent with the formic acid covered surface25. The iron z displacements into the bulk in the subsurface layers are consistently higher for OLAC than for formic acid which is due to the stronger interaction of OLAC with the surface, compare Table We obtained quantitative information on the OLAC adsorption-induced near-surface structural changes from SXRD. A line scan through reciprocal space at 21. In all these cases, atomic hydrogen plays a key role, which indicates that in the case of OLAC adsorption the carboxylic end group dissociates and the released H induces the near-surface cation redistribution. As a result, the bulk stoichiometry of the surface is almost reestablished during the OLAC adsorption process (the SCV reconstructed surface is oxygen rich), without further vacancy formation at the interface. Such a vacancy formation was observed in contrast to the interaction of water vapor and atomic hydrogen with the (001) surface.The occupation profile of the different atomic layers obtained from the fit is shown in Fig. 26 is shown in Fig. tet1 surface termination23, together with 20% Fe vacancies in the topmost layer. For the fit of the OLAC exposed surface, a carboxylic end group was placed in quasi-bidentate geometry. The structure factor difference is negligible when it is placed into the chelating site.In the following, we will compare with OLAC adsorption-induced structural changes of the magnetite 111) surface. The LEED pattern of the clean (111) surface, terminated by tetrahedrally coordinated Fe ions11 surfacz-displacement of the Fetet1 surface layer is reduced after adsorption to −0.079 Å (clean surface: −0.134 Å). A similar trend of the topmost Fe and O ions showing smaller relaxations after adsorption was also observed for OLAC on magnetite (001) and indicates that the surface can now compensate its polarity by charged adsorbates instead of purely relaxations in the case of the clean surface. After adsorption of OLAC , we find lower subsurface iron and oxygen occupancies of the first four layers compared to the clean surface, see Fig. 21 on magnetite (001) and formic acid on magnetite (111)27. It might be beneficial for the OLAC layer growth since more active sites, edge and corner atoms, are available at the surface. Thus our experiments show that as a result of the vacancy formation, additional iron and oxygen vacancies are formed on magnetite (111) after adsorption. This is in contrast to the filling of vacancies (lifting of the SCV reconstruction) after the adsorption on magnetite (001)17. The fitted atomic positions and occupancies of the clean (111) surface and after OLAC adsorption are given in Tables The −6 mbar, 60 min at room temperature) on the (001) and (111) magnetite surfaces. After OLAC adsorption, there is for both surfaces a distinct oscillation visible in the XRR curves, which results from constructive and destructive interference due to the electron density difference of the adsorbate layer and the substrate. The oscillation is more pronounced for the (111) surface and shifted to lower momentum transfer q values, indicating the formation of a thicker OLAC layer. On the (001) surface, the layer was fitted to a thickness of 8.2 ± 0.8 Å with an electron density of 0.15 ± 0.02 e/Å3. This corresponds to 0.76 molecules/nm2. Our previous studies18 show that formic acid adsorbs with two molecules per unit cell on magnetite (001) (2.8 molecules/nm2). A higher molecule density can be achieved for formic acid because of the smaller molecule size. In addition, the orientation of the aliphatic chain of OLAC has to be taken into account as it is effectively blocking adsorption sites on magnetite (001), see FT-IR and MD sections below. The XRR fit parameters are given in Table Morphological information on the adsorbed OLAC layers can be obtained by X-ray reflectivity (XRR) independent of their structural order. Figure e/Å3, see inset in Fig. e/Å3. The fitted density corresponds to 2.33 OLAC molecules/nm2. The fitted layer thickness suggests a more upright orientation of the molecules (length: 20 Å) on the (111) surface as compared to the (001) surface. The electron density and thickness of the OLAC layer are higher on the (111) surface suggesting a different adsorption behavior than on the (001) surface. A surface coverage of 1.9 OLAC molecules/nm2 was reported on magnetite nanoparticles after a thermal treatment at 150 °C1, in good agreement with our results, considering a mixture of (001) and (111)-type facets in the nanocomposite material. Unfortunately, no additional information of the surface morphology could be obtained from STM, see Supplementary Notes On the (111) surface, the OLAC layer thickness was determined to be 14.1 ± 1.4 Å with an electron density of 0.26 ± 0.03 p-polarized spectra acquired after OLAC adsorption on magnetite (001) are shown in Fig. −1 and 1364 cm−1. Both bands exhibit a Fano-type line shape, which results from magnetite being neither a perfect metal nor a perfect insulator28. The band at 1364 cm−1 presents a line shape which is expected for a dynamical dipole perpendicular to the surface23. The one at 1560 cm−1 has an inverted line shape which originates from a dynamical dipole parallel to the surface. Both bands appear at similar positions as observed after 2 L (1 Langmuir = 1.33 × 10−6 mbar × s) formic acid exposure on magnetite (001) (1544 and 1368 cm−118). On magnetite (001) they were assigned to the asymmetric and symmetric stretching bands νas and νs(OCO) of formate in a bidentate bridging adsorption geometry. OLAC stabilized magnetite nanoparticles show vibrational bands at 1603 cm−1 and 1377 cm−1 which were assigned to the same stretching modes1. Hence, we conclude that for low coverage OLAC dissociates to oleate (C17H33COO−) and binds with its carboxylic end group to the magnetite (001) surface in the same bidentate bridging configuration as formic acid does: with two oleate oxygen atoms bound to two octahedral iron ions on the surface. The larger splitting between νas and νs in the case of OLAC (196 cm−1) compared to formic acid (176 cm−1) is, however, not reflected in a larger OCO bond angle of oleate . This trend was seen for formate in different adsorption geometries on the Fe3O4(111) surface23, indicating that also the aliphatic tail has an influence on the splitting. With increasing OLAC coverage the two νs(OCO) bands became broader, overlap and shift, likely due to increasing disorder and intermolecular interaction. An additional band at 1707 cm−1 appears for higher exposure and is assigned to the C=O stretching vibration of non-dissociated OLAC molecules18. The shape of this band is the same as for νs(OCO), hence resulting from a net dynamical dipole perpendicular to the surface. Along with the initial dissociation of OLAC on magnetite (001) we observe pronounced adsorption of non-dissociated molecules with the C=O bond in a preferred orientation . Two additional bands at 2936 cm−1 and 2856 cm−1 appear at higher exposure and are assigned to νas and νs stretching vibrations of CH2.FT-IRRAS measurements were performed on both surfaces to elucidate the binding geometry of OLAC. The -7 mbar for 4 min a broad contribution with its maximum centered around 1470 cm−1 appears, which further grows at higher exposures. This broad band can also be observed in the high coverage spectra of OLAC on the (001) surface, where it was assigned to an overlap of the νs(OCO) and νas(OCO) of the carboxylic end group. We therefore suggest that the OLAC carboxylic end group also dissociates on the magnetite (111) surface at room temperature and that the νas(OCO) and νs(OCO) overlap due to higher intermolecular interaction. The dissociation of formic acid was observed as well on magnetite (111) and formate in quasi-bidentate and chelating adsorption geometries, giving rise to three pronounced bands in the ν(OCO) stretching region23. Another band appears at 1787 cm−1 after an exposure to 2 × 10−7 mbar for 4 min and is attributed to the ν(C=O) stretching vibration of non-dissociated OLAC molecules. The shape of this band matches the line shape that is expected for a dynamical dipole parallel to the surface which is in direct contrast to the perpendicular orientation on magnetite (001). Similar to the (001) surface, the theoretical OCO bond angle is smaller for oleate compared to formate in identical adsorption geometries (chelating: 112.4° for oleate (MD), compared to 120° for formate (DFT); quasi-bidentate: 115.5° for oleate, compared to 128° for formate, see Table 23). The smaller OCO bond angles for oleate are likely due to the larger molecule size and increasing molecule disorder at higher coverages. Two bands with a small amplitude appear around 2923 cm−1 and 2850 cm−1. These bands are assigned to the asymmetric and symmetric ν(CH2) stretching vibrations, respectively, similar to the (001) surface. The band position in both cases is slightly reduced compared to the free molecule13 due to the limited mobility of the alkyl chains.The FT-IRRAS results after OLAC exposure of the clean (111) surface are shown in Fig. 2 quickly forms a monolayer on the (001) surface which is in good agreement with FT-IRRAS results.At early stages of the ligand adsorption, molecule-molecule interactions play only a minor role and OLAC finds an adsorption site on both surfaces almost immediately upon iterative deposition. Every new attempt to add OLAC to the system leads thus to a dissociative adsorption of OLAC to oleate  or the C–OA bond of oleates (blue dots) and the (001) surface at different coverages are shown in Fig. 2 the distribution of Θ on (001) starts to shift to larger angles in which molecularly adsorbed OLAC binds to a Feoct via the oxygen of the C=OA group while establishing a hydrogen bond to another oleate already adsorbed on the surface  band at 1707 cm−1 indicates that the C=O bond is oriented more perpendicular to the surface for higher ligand coverages. In general, we expect that simulations comprising a larger surface area are more sensitive to the detection of molecular adsorption of OLAC and that it will start even earlier. The hydrogen bond formed between a molecularly adsorbed OLAC and oleate (1.57 Å) is significantly shorter than hydrogen bonds formed between molecularly adsorbed OLAC and surface oxygens (2.53 Å). Up to a coverage of 1.50 molecules/nm2, the aliphatic chains lie rather flat on the (001) surface and are disordered  two different slopes appear which we attribute to the change from a quasi-bidentate to a chelating adsorption. Thereafter, the geometric criterion of dissociation is no longer satisfied, and we observe molecular adsorption of oleic acid forming a hydrogen bond to a nearby oleate. We stopped adding new oleic acids when we could no longer observe this molecular adsorption.Above this coverage, the time required for newly introduced OLAC to find an adsorption site where it can dissociatively adsorb to oleate increases dramatically and molecular adsorption  surface and are shown in the inset of Fig. Electron density profiles were calculated for the experimentally determined coverage of 0.8 molecules/nmon the 00 surface A) binds to a Fetet1 and the other oxygen binds via a hydrogen bond to the closest surface hydroxyl  via a hydrogen bond to another adsorbed quasi-bidentate oleate. The angles of the different adsorbed OLAC and oleate groups behave as Θchelate > Θquasi-bidendate ≥ Θmolecular and an angle of 15° was found for the C=O bond of molecularly adsorbed OLAC  leads to less steric hindering of the carboxylic groups adsorbed on the surface  and (111). The determination of dissociative and molecular adsorption for certain OLAC coverages as well as molecule orientation on the two different magnetite surfaces show a high level of agreement between the MD simulations and experimental results making this approach very useful to study the organic acid/oxide interface on the atomic scale.Depending on the coverage, also for the (111) surface different adsorption motifs of molecularly adsorbed OLAC and oleates are observed cf. Fig. f. A highace Fig. . Consequ24, formate molecules exhibit a high mobility at finite temperatures, which can also be a reason for the easier reorganization of the OLAC layer on this surface and thus higher coverages than on the (001) surface. The layer growth of OLAC is much easier on the (111) surface, because proton transfer reaction rates are higher due to the quasi-bidentate and chelating adsorption of oleate leaving enough possible adsorption sites for further agglomeration of FeO(OH) precursor molecules in actual OLAC stabilized magnetite NP composites. This is in line with the annealing behavior in Dreyer et al.1, where more (111)-type facets were formed, increasing mechanical strength of the nanocomposite material. The mechanical properties of such composites can be tailored by nanoparticle shape change towards more (111)-type facets and avoiding (001)-type facets since a higher molecule density can be achieved on the former. An accurate atomistic understanding of the interface between magnetite and organic acids is a prerequisite for future upcoming simulations which are able to predict the mechanical properties of nanocomposite bulk materials. While it should be noted that the force field used is capable of accurately describing the stability of common surface terminations on magnetite and even accurately describing the interface with small organic molecules24, it should be noted that the simple force field used here cannot incorporate explicit quantum mechanical effects such as magnetism or the specific conductivity of magnetite and provides only a limited representation of the complex charge ordering in magnetite8. Nevertheless, our combined microscopic, experimental, and theoretical view of the magnetite/organic acid interface opens up the possibility to tune the mechanical properties of nanocomposite materials and brings this within reach thanks to the availability of reliable force fields and fundamental experiments elucidating this complex interfacial interplay.In summary, we studied the adsorption of OLAC on the single-crystalline magnetite (001) and (111) facets using FT-IRRAS, LEED, SXRD, and XRR in combination with MD simulations employing a new force field specifically developed for magnetite/organic interfaces. The interfacial properties of OLAC on both magnetite surfaces have distinct differences regarding molecule coverage, layer thickness, binding geometry, and interfacial defects: at low coverage, OLAC dissociates upon adsorption on the magnetite (001) surface at room temperature and adsorbs in a bidentate bridging geometry, just like formic acid does on this surface . The bulk stoichiometry is restored after adsorption of OLAC on a (001) surface and for a higher OLAC coverage, non-dissociated molecules adsorb at the surface with the C=O bond oriented perpendicular to the surface and a flat adsorption of the aliphatic OLAC tail, effectively blocking adsorption sites. On magnetite (111), again both dissociative and molecular adsorption of OLAC are observed, however, the latter has a C=O bond parallel to the surface which results in higher thickness and molecular density of the OLAC layer. As previously observed on magnetite (111)3) was prepared with multiple cycles of Ar+ sputtering  followed by UHV annealing at 650 °C both for 15 min. The last annealing cycle was done in 1 × 10−6 mbar oxygen at 650 °C. Magnetite (111) was prepared by multiple cycles of Ar+ ion sputtering  for 10 min, followed by annealing at 700 °C for 10 min in an oxygen partial pressure of The natural magnetite (001) single crystal  with 512 scans with a resolution of 2 cm31 using a 2D detector in stationary mode32 and a photon energy of 14 keV. The base pressure was 2 × 10−10 mbar. The data integration was done using BINoculars33. The data was then fitted to several structural models using ROD from the ANAROD package34. The SXRD data presented here are referenced according to the cubic surface unit cell of magnetite (001) described by the following parameters: a = b = c = 8.394 Å along the ,  and  bulk directions, respectively, using α = β = γ = 90°. The magnetite (111) SXRD data are referenced to the hexagonal surface unit cell with the following parameters: a = b = 5.935 Å, c = 14.539 Å along the [1,α = β = 90° and γ = 120°. Additional information is given in the Supplementary Notes λ = 0.8856 Å (Eγ = 14 keV) and the incidence angle θ. The electron density profiles, which are calculated from the fit, are plotted in e/Å3 as a function of the distance perpendicular to the surface z, as shown in the insets of Fig. 30 using a three-layer model consisting of OLAC, an intermediate magnetite layer, and a bulk magnetite layer. The intermediate layer has a reduced electron density compared to bulk magnetite and can be explained by island growth during annealing35. The XRR fit parameters can be found in Supplementary Notes The SXRD and XRR data were acquired at the ID03 beamline of the European Synchrotron Radiation Facility (ESRF)36 utilizing a Nosé-Hoover thermostat (NVT)38. Equations of motion are integrated with timesteps of 0.5 fs in the velocity-Verlet algorithm. A standard Ewald summation39 with a precision of 10−6 and a cutoff of 12 Å was used to solve long-range electrostatic interactions. An in-house code which automatically assigns partial charges based on an ab initio Bader charge analysis performed on magnetite surface atoms is used together with a modified CLAYFF parameter set41 to describe the magnetite-organic interface. It should be noted that for each specific dissociative adsorption motif on magnetite a specific partial charge set is used. Thus, it is implicitly accounted for the rather complex oxidation state of magnetite by considering a different partial charge scheme for magnetite depending on the particular situation.All MD simulations were performed using LAMMPS24. OLAC and oleate are described by the General AMBER Force Field (GAFF)42 and partial charges have been optimized by a two-step restrained electrostatic potential fit charge model (RESP)43 to obtain partial point charges for OLAC and oleate which reproduce the electrostatic potential from QM calculations using the B3LYP functional method, a polarizable water model(c-PCM), and a cc-pV(T+d)Z basis set (more details are found in Lundborg and Lindahl44). To obtain partial point charges, topologies and corresponding parameters practically, the Small molecule Topology GEnerator (STaGE)44 is used, which has a built-in RESP implementation and calculates the desired charges from simple SMILES (Simplified Molecular Input Line Entry Specification). Magnetite surface slabs are generated by replicating a 4 × 4 × 2 bulk unit cell, cleavage and removal of additional iron atoms at vacancies for SCV. Only along x- and y-directions, periodic boundary conditions are applied.Parameters and partial point charges used in the empirical force field can be found in Konuk et al.29) to ensure that the formation of the monolayer is not significantly perturbed. Nevertheless, it could have some influence and explain the higher than experimentally observed coverage of the (001) surface, as the disordered ligand structure is readily penetrated. We assumed in all (001) simulations a distorted, bulk-truncated (DBT) structure as a starting point, although starting from a SCV surface might be a better initial choice in case of no or low ligand surface coverage. We rationalize this decision by previous experimental observations21 stating that the SCV surface is already lifted at very low ligand coverages. After OLAC reaches the magnetite surface it forms hydrogen bonds with suitable surface oxygens. Hydrogen bonds of OLAC to other OLAC or surface oxygens as well as oleates are estimated using a simple distance threshold between acceptor and hydrogen of 2.5 Å, which has already been shown to be sufficient for a similar setup45. A proton transfer was initiated inspired by the works of refs. Dietrich et al. 45 and previously adapted for the adsorption of formic acid on magnetite surfaces24. Similarly, the proton transfer reaction of the dissociative adsorption is triggered when the distance of a carboxylic hydrogen to a surface oxygen is ≤2.5 Å. Consequently, OLAC is allowed to adsorb and eventually dissociate until a certain coverage is achieved. After reaching a coverage of around 2.23 molecules/nm2, the highest observed coverage on both (001) and (111) surfaces in our MD simulations, no further initial (molecular) adsorption of OLAC was observed. Dissociative adsorption of OLAC on Fetet1-terminated magnetite (111) is modeled similarly to the adsorption on a (001) surface. Except that in the case of a (111) surface, we assume that after dissociation of OLAC, the hydrogen continues to migrate to the second nearest oxygen on the magnetite (111) surface after an initial adsorption to the nearest oxygen24. Analyses for different coverages were carried out on 1 ns long simulations at 300 K picking a snapshot after a certain coverage was reached.After the magnetite surfaces have been thermally pretreated at room temperature, OLAC molecules are added iteratively every 100 ps from a random starting position 20 Å away from the magnetite surface with random thermal velocities corresponding to the temperature in the experiments. The carboxylic group of OLAC is pulled to the surface only during the first 7 ps of deposition with a constant force of 0.33 nN using steered MD. The pulling force used in our simulation was carefully chosen and is weaker than in a previous work  are used to smear the atomic point charges, which would otherwise produce a rather artificially noisy signal. Each profile was normalized to one and multiplied with the corresponding theoretical number of total electrons. Individual calculated profiles are combined to obtain the total electron density of the system and are given in e/Å3 as a function of the distance perpendicular to the surface.Electron densities are obtained using the Density Profile Tool in VMDSupplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2