PW06-05 The predictive role of anxiety disorders on depressive phenomenology during post-partum period

Aims:To investigate the predictive role of any specific (DSM-IV) Anxiety Disorders (AD) on depressive symptoms and Major or Minor Depressive Disorder (MDD, mDD) during early postpartum period.Method:Women (at the 12th-15th gestational week, N=1066) were recruited in the framework of the Program 'Perinatal Depression - Research & Screening Unit (PND-ReScU)". Depressive symptoms were assessed by the Edinburgh Postnatal Depression Scale (EPDS), and Axis-I disorders (AD, MDD, mDD) were diagnosed with the Structured Clinical Interview for Axis-I Disorders (SCID-I).Results:Any current AD at baseline (3rd month of pregnancy) was detected in 231 (21.7%). Having at least one current AD, was associated with a greater likelihood of having MDD or mDD during the early postpartum period, even after the adjustment for the confounding factor of having a lifetime history of MDD (RR=3.86 95%CI 1.58-9.42).In particular, women affected by Obsessive Compulsive Disorder (N=17; 1.6%) or Panic Disorder (N=43; 4%) had at higher risk to develop depressive symptoms (EPDS≥13) during the postpartum period than women without these AD (RR=6.9 and 6.7 respectively). As for the risk of developing PPD, the strongest association was found for women with Panic Disorder (RR=7.6 95% CI 2.62-22.0).Conclusions:AD are associated with a greater likelihood to develop depressive symptoms and MDD or mDD during the early postpartum period. Women with current PD have the strongest risk to develop both MDD or mDD and depressive symptoms during early postpartum period compared to other anxiety disorders

Ultrafast Delamination of Graphite into High-Quality Graphene Using Alternating Currents

To bridge the gap between laboratory‐scale studies and commercial applications, mass production of high quality graphene is essential. A scalable exfoliation strategy towards the production of graphene sheets is presented that has excellent yield (ca. 75 %, 1–3 layers), low defect density (a C/O ratio of 21.2), great solution‐processability, and outstanding electronic properties (a hole mobility of 430 cm2 V−1 s−1). By applying alternating currents, dual exfoliation at both graphite electrodes enables a high production rate exceeding 20 g h−1 in laboratory tests. As a cathode material for lithium storage, graphene‐wrapped LiFePO4 particles deliver a high capacity of 167 mAh g−1 at 1 C rate after 500 cycles

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42, 400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences. © 2021, The Author(s)

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

AbstractLarge datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.</jats:p