Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Carbon nanotube. The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

Planck intermediate results: LI. features in the cosmic microwave background temperature power spectrum and shifts in cosmological parameters

Abstract: The six parameters of the standard ΛCDM model have best-fit values derived from the Planck temperature power spectrum that are shifted somewhat from the best-fit values derived from WMAP data. These shifts are driven by features in the Planck temperature power spectrum at angular scales that had never before been measured to cosmic-variance level precision. We have investigated these shifts to determine whether they are within the range of expectation and to understand their origin in the data. Taking our parameter set to be the optical depth of the reionized intergalactic medium τ, the baryon density ωb, the matter density ωm, the angular size of the sound horizon θ∗, the spectral index of the primordial power spectrum, ns, and Ase− 2τ (where As is the amplitude of the primordial power spectrum), we have examined the change in best-fit values between a WMAP-like large angular-scale data set (with multipole moment l 800, or splitting at a different multipole, yields similar results. We examined the l 800 power spectrum data and find that the features there that drive these shifts are a set of oscillations across a broad range of angular scales. Although they partly appear similar to the effects of enhanced gravitational lensing, the shifts in ΛCDM parameters that arise in response to these features correspond to model spectrum changes that are predominantly due to non-lensing effects; the only exception is τ, which, at fixed Ase− 2τ, affects the l> 800 temperature power spectrum solely through the associated change in As and the impact of that on the lensing potential power spectrum. We also ask, “what is it about the power spectrum at l < 800 that leads to somewhat different best-fit parameters than come from the full l range?” We find that if we discard the data at l < 30, where there is a roughly 2σ downward fluctuation in power relative to the model that best fits the full l range, the l < 800 best-fit parameters shift significantly towards the l < 2500 best-fit parameters. In contrast, including l < 30, this previously noted “low-l deficit” drives ns up and impacts parameters correlated with ns, such as ωm and H0. As expected, the l < 30 data have a much greater impact on the l < 800 best fit than on the l < 2500 best fit. So although the shifts are not very significant, we find that they can be understood through the combined effects of an oscillatory-like set of high-l residuals and the deficit in low-l power, excursions consistent with sample variance that happen to map onto changes in cosmological parameters. Finally, we examine agreement between PlanckTT data and two other CMB data sets, namely the Planck lensing reconstruction and the TT power spectrum measured by the South Pole Telescope, again finding a lack of convincing evidence of any significant deviations in parameters, suggesting that current CMB data sets give an internally consistent picture of the ΛCDM model.Key words: cosmology: observations / cosmic background radiation / cosmological parameters / cosmology: theory

Hydrophobic, Superabsorbing Aerogels from Choline Chloride-Based Deep Eutectic Solvent Pretreated and Silylated Cellulose Nanofibrils for Selective Oil Removal

Abstract: Superabsorbents are highly appealing materials for use in cleaning up oil and chemical spills. However, the development of a low-cost, highly efficient superabsorbent remains a major challenge. This paper demonstrates a straightforward method of producing a cellulose nanofibril aerogel that is low-cost, ultralight, highly porous, hydrophobic, and reusable superabsorbing cellulose nanofibril aerogel from recycled waste fibers using a simple, environmentally friendly nanofibrillation treatment involving deep eutectic solvent and freeze-drying. Nanofibrillation and hydrophobic modification (silylation) of waste cellulose fibers resulted in nanofibril sponges with ultralow density (0.0029 g/cm3) and high porosity (up to 99.81%) after freeze-drying. These sponges exhibited excellent absorption performances for various oils and organic solvents and were reusable. In particular, the nanofibril aerogels showed selectivity in absorbing marine diesel oil from an oil–water mixture and possessed ultrahigh absorption ...

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.

Abstract: Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications.