Polymers crosslinked via furan/maleimide thermo-reversible chemistry have been extensively explored as reprocessable and self-healing thermosets and elastomers. For such applications, is important that the thermo-reversible features are reproducible after many reprocessing and healing cycles. Therefore, side reactions are undesirable. However, we have noticed irreversible changes in the mechanical properties of such materials when exposing them to temperatures around 150 °C. In this work, we study if these changes are due to the self-reaction of maleimide moieties that may take place at this rather low temperature. In order to do so, we prepared a furan-grafted polyketone crosslinked with the commonly used aromatic bismaleimide (1,1'-(methylenedi-4,1-phenylene)bismaleimide), and exposed it to isothermal treatments at 150 °C. The changes in the chemistry and thermo-mechanical properties were mainly studied by infrared spectroscopy, 1H-NMR, and rheology. Our results indicate that maleimide self-reaction does take place in the studied polymer system. This finding comes along with limitations over the reprocessing and self-healing procedures for furan/maleimide-based reversibly crosslinked polymers that present their softening (decrosslinking) point at relatively high temperatures. On the other hand, the side reaction can also be used to tune the properties of such polymer products through in situ thermal treatments.
Here is the raw and processed data obtained by FTIR, NMR and rheology. Each excel file is clearly named in order to guide the reader.
Code and data used in the publication "Identification of the Dominant Recombination Process for Perovskite Solar Cells Based on Machine Learning"
The dataset saved in the .csv files "balanced_dataset.csv" consist of over 106 simulated perovskite solar cells with their performance (VOC,JSC and FF) at (0.1,0.18,032,0.56,1) sun illuminations as well as the ideality factor (n) .
This dataset was built using drift-diffusion open-source code SIMsalabim. (https://github.com/kostergroup/SIMsalabim)
We present a manually annotated Bangla Emotion corpus, which incorporates the diversity of fine-grained emotion expressions in social-media text. We tried to consider more fine-grained emotion labels such as Sadness, Happiness, Disgust, Surprise, Fear and Anger - which are, according to Paul Ekman (1999), the six basic emotion categories. For this task, we collected a large amount of raw text data from the user’s comments on two different Facebook groups (Ekattor TV and Airport Magistrates) and from the public post of a popular blogger and activist Dr. Imran H Sarker. These comments are mostly reactions to ongoing socio-political issues and towards the economic success and failure of Bangladesh. We scrape a total of 32923 comments from the three sources aforementioned above. Out of these, a total of 6314 comments were annotated into the six categories. The distribution of the annotated corpus is as follows:
sad = 1341
happy = 1908
disgust = 703
surprise = 562
fear = 384
angry = 1416
We have also provided a balanced set from the above data and split the dataset into training and test set of equal ratio. We considered a proportion of 5:1 for training and evaluation purpose. More information on the dataset and the experiments on it could be found in our paper (related links below).
Contributors:Christian Dorninger, Stefan Giljum, John-Oliver Engler, Hanspeter Wieland, david abson et al
The excel file contains all data used to construct the structural equation models for 2015. This includes data on population, gross national income, biophysical reserves, the technology index, military expenses, net imports of raw material equivalents, embodied energy, land, and labor, and the value added of raw material equivalents exported, as well as that of embodied energy exported, embodied land, and labor.
The Kälin and Kochenov Quality of Nationality Index (QNI) ranks the objective quality of nationalities worldwide. It explores three internal factors (economic strength, human development, and peace and stability) and four external factors (diversity and weight of travel freedom and diversity and weight of settlement freedom) which are used to measure the value of virtually all nationalities worldwide. Peace and stability counts for 10% of aggregate value, all other six factors count for 15% each. The QNI has been created by Dr. Christian H. Kälin, Chairman of Henley & Partners, and Prof. Dimitry Kochenov, Professor of European Constitutional Law and Citizenship at the University of Groningen. This dataset is the basis of the Kälin and Kochenov Quality of Nationality Index, edited by Dimitry Kochenov and Justin Lindeboom (Hart Publishing 2019).
Measurement and sources:
1) Economic Strength of the country conferring the nationality is measured by GDP, excluding NRR, with power purchasing parity (PPP). GDP with PPP and NRR have been collected from the World Bank. All figures are normalized to a 0-15% scale.
2) Peace and Stability of the country conferring the nationality is measured by reference to the Global Peace Index. All figures are normalized to a 0-10% scale.
3) Human Development of the country conferring the nationality is measured by reference to the UN Human Development Index. All figures are normalized to a 0-15% scale.
4) Diversity of Settlement Freedom refers to the number of foreign countries in which a nationality's holders can freely settle (including the right to work there) without having to obtain a visa or with visa-on-arrival. All figures are normalized to a 0-15% scale. Data is gathered through extensive research with the assistance of regional experts.
5) Weight of Settlement Freedom measures the qualitative value of the foreign countries in which a nationality's holder is allowed to settle freely. Each settlement destination is valued by reference to its Economic Strength and Human Development. The aggregate value of all settlement destinations determines a nationality's weight of settlement freedom. All figures are normalized to a 0-15% scale.
6) Diversity of Travel Freedom measures the number of destinations to which a nationality's holder can travel to visa-free or with visa-on-arrival. All figures are normalized to a 0-15% scale. This data is provided by the International Air Transport Association (IATA).
7) Weight of Travel Freedom measures the qualitative value of visa-free and visa-on-arrival travel destinations, and also relies on data provided by IATA. Each travel destination is valued by reference to its Economic strength and Human Development. The aggregate value of all travel destinations determines a nationality's weight of travel freedom. All figures are normalized to a 0-15% scale.
This dataset contains metadata collected for the purpose of the QNI from 2011 to 2018, as well as the resulting rankings.