Tritium label in studying sorption of humic substances by carbon-based nanomaterials

Sorption capacity of single-walled carbon nanotubes, detonated nanodiamonds and graphene to humic substances was studied by radiotracer method. Tritium labeled brown coal humic acids and fulvic acids separated from Suwannee River were used as sorbates. Adsorption isotherms were described by Langmuir equation. It was found that, for all tested carbon-based nanomaterials, adsorption of coal humic acids is higher than of river fulvic acids. Adsorption capacity of nanomaterials in attitude to humic substances was changed in the order, nanodiamonds < single-walled nanotubes < graphene. Composites of humic substances with carbon-based nanomaterials were subjected to dynamic light scattering analysis

Radiochemical Study of Biopolymers Sorption on Hydrophobic Surfaces

The behavior of globular proteins (lysozyme, human serum albumin) and humic acids of coal (Powhumus) in aqueous/oil and aqueous/graphene systems was studied by means of tritium tracer. Tritium labeled biomolecules were obtained by tritium thermal activation method. Adsorption isotherms were obtained by liquid scintillation spectrometry of tritium either in traditional performance or in scintillation phase techniqueМетод радіоактивних індикаторів був застосований для дослідження поведінки глобулярних білків (лізоцим, сироватковий альбумін людини) та гумінових кислот вугілля (Powhumus) в системах вода/олія та вода/графен. Мічені тритієм біополімери були одержані методом термічної активації тритію. Ізотерми адсорбції біологічних макромолекул на межі поділу вода/графен і на міжфазній границі вода/олія були отримані за допомогою відповідно рідинно-сцинтиляційної спектрометрії тритію в традиційному варіанті та методом сцинтилюючої фази.Метод радиоактивных индикаторов был применен для исследования поведения глобулярных белков (лизоцим, сывороточный альбумин человека) и гуминовых кислот угля (Powhumus) в системах вода/масло и вода/графен. Используемые меченые тритием биополимеры были получены методом термической активации трития. Изотермы адсорбции биологических макромолекул на поверхности раздела вода/графен и межфазной границе вода/масло были найдены с помощью жидкостной сцинтилляционной спектрометрии трития в традиционном варианте и в варианте метода сцинтиллирующей фазы соответственно

Data for: Scaling-Up Grey-Box Models for Predicting Building Dynamics - Model Structure, Seasonal Variation, and Physical Interpretability

The data accompany the article Scaling-Up Grey-Box Models for Predicting Building Dynamics: Model Structure, Seasonal Variation, and Physical Interpretability, which presents an elaborate analysis of different RC model structures, different training data, different validation data, and different model selection methods. The training and validation data have been acquired by using energy modeling software TRNSYS for a building located in Zagreb. One year’s worth of data has been divided into 52 training periods, each lasting 7 days, and 182 validation periods, each lasting 2 days. Models were trained by using a combination of Latin Hypercube Sampling (LHS) and non-linear least squares. For the LHS, the number of initial parameter guesses was varied, resulting in 7 different parameter guesses densities: 2, 5, 10, 20, 50, 100, 200. Each of those guess densities was used to produce that many models for each of the training periods. That means that if the number of initial guesses is 200, there were 200 models trained for each training period, resulting in 200x52=10 400 models. The data with a timestep of 15 minutes used for training and validating the models can be found in the folder “0. Training and validation data”. Parameter values, RMSE for training periods and RMSE for validation periods can be found in folders “1. SE zone”, “2. SW zone”, “3. NW zone”, “4. NE zone”. The platform in which the user can interact with RC models to increase and decrease model parameters can be found in the folder “5. Platform for RC Model Parameter Analysis”. Each folder contains a description file regarding the data. Any additional inquiry about the data set and the RC model platform can be sent to the corresponding author, Nikola Badun ([email protected])

Data for: Scaling-Up Grey-Box Models for Predicting Building Dynamics - Model Structure, Seasonal Variation, and Physical Interpretability

The data accompany the article Scaling-Up Grey-Box Models for Predicting Building Dynamics: Model Structure, Seasonal Variation, and Physical Interpretability, which presents an elaborate analysis of different RC model structures, different training data, different validation data, and different model selection methods. The training and validation data have been acquired by using energy modeling software TRNSYS for a building located in Zagreb. One year’s worth of data has been divided into 52 training periods, each lasting 7 days, and 182 validation periods, each lasting 2 days. Models were trained by using a combination of Latin Hypercube Sampling (LHS) and non-linear least squares. For the LHS, the number of initial parameter guesses was varied, resulting in 7 different parameter guesses densities: 2, 5, 10, 20, 50, 100, 200. Each of those guess densities was used to produce that many models for each of the training periods. That means that if the number of initial guesses is 200, there were 200 models trained for each training period, resulting in 200x52=10 400 models. The data with a timestep of 15 minutes used for training and validating the models can be found in the folder “0. Training and validation data”. Parameter values, RMSE for training periods and RMSE for validation periods can be found in folders “1. SE zone”, “2. SW zone”, “3. NW zone”, “4. NE zone”. The platform in which the user can interact with RC models to increase and decrease model parameters can be found in the folder “5. Platform for RC Model Parameter Analysis”. Each folder contains a description file regarding the data. Any additional inquiry about the data set and the RC model platform can be sent to the corresponding author, Nikola Badun ([email protected]).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV