scispace - formally typeset
Search or ask a question
Institution

Tallinn University of Technology

EducationTallinn, Estonia
About: Tallinn University of Technology is a education organization based out in Tallinn, Estonia. It is known for research contribution in the topics: European union & Oil shale. The organization has 3688 authors who have published 10313 publications receiving 145058 citations. The organization is also known as: Tallinn Technical University & Tallinna Tehnikaülikool.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, a MIP-based electrochemical sensor for detection of SARS-CoV-2 nucleoprotein (ncovNP) was presented for the first time.

53 citations

Journal ArticleDOI
TL;DR: In this article, the effects of nanoconfinement on the dehydrogenation rate and reaction pathways of the eutectic LiBH4-Mg(BH 4)2 have been comprehensively investigated.
Abstract: The effects of nanoconfinement on the dehydrogenation rate and reaction pathways of the eutectic LiBH4–Mg(BH4)2 have been comprehensively investigated. By means of thermal analysis, mass spectroscopy and solid state 11B MAS NMR, it has been revealed that the multistep thermal decomposition pattern of the binary LiBH4–Mg(BH4)2 has been altered in a two-step reaction and the desorption kinetics has also been significantly improved after infiltration. The formation of diborane and stable MnB12H12 intermediates of the bulk LiBH4–Mg(BH4)2 has been found to be inhibited by nanoconfinement.

53 citations

Journal ArticleDOI
TL;DR: In order to reconcile the management of waste to the scale of its production, several researches are being pursued. as mentioned in this paper provides an insight into these developments, along with the critical discussion, limitations and economic feasibility of waste valorization technologies to provide new understanding for the advancement of bioeconomy.
Abstract: The world today is not only facing the problem of depleting energy sources but also generation of waste from anthropogenic activities. While waste is a risk, it is also an opportunity to solve this dual problem through utilization of waste as a potential source of energy and products. In order to reconcile the management of waste to the scale of its production, several researches are being pursued. Establishment of bioeconomy is a great way to achieve this goal. But inherent challenges associated with biowaste include manageability of by-products and sludge, wide variety in waste composition, efficiency of the process and economic viability of treatment technologies to scale-up and industrialize beyond laboratory setup. Recent advancements have been made in this regard with the use of new techniques, synergistic catalysts, combination of technologies and novel treatment materials to remediate the challenges and maximize the value of waste by utilizing it as a feedstock to produce industrial chemicals, fuels and materials. This review provides an insight into these developments, along with the critical discussion, limitations and economic feasibility of waste valorization technologies to provide new understanding for the advancement of bioeconomy.

53 citations

Journal ArticleDOI
TL;DR: In this article, the authors used hierarchical partitioning to identify local factors (habitat area and heterogeneity, grazing intensity, habitat continuity) and landscape factors (proportion of surrounding grassland in 2004, 1938 and 1800) associated with the richness estimates.
Abstract: Questions: To what extent is species richness in semi-natural grasslands related to local environmental factors and (present/past) surrounding landscape structure? Do responses of species richness depend on degree of habitat specialization (specialists vs generalists) and/or scale of the study? Location: Oland, Sweden. Methods: Richness of herbaceous vascular plants (subdivided into richness of grassland specialists and generalists) was recorded within 50 9 50 cm plots and 0.1–4.8 ha grassland polygons. Generalized linearmodels and hierarchical partitioning were used to identify local factors (habitat area and heterogeneity, grazing intensity, habitat continuity) and landscape factors (proportion of surrounding grassland in 2004, 1938 and 1800, and landscape diversity in 2004)associated with the richness estimates. Results: At the polygon scale, both specialist and generalist richness was positively associated with local habitat area and heterogeneity and, independently of area and heterogeneity, with grazing intensity, habitat continuity and amount of surrounding grassland in 1800. At the plot scale, specialist species richness was positively associated with habitat heterogeneity, amount of surrounding grassland in 2004 and landscape diversity. Plot-scale generalist richness was negatively associated with surrounding grassland in 1938 and positively associated with local grazing intensity. Conclusions: Because both habitat specialization and study scale influence conclusions about relationships between species richness and local and landscape factors, the study highlights the need to consider species diversity at multiple spatial scales when making decisions about grassland management. Large-scale(polygon) species richness is influenced by immigration processes, with both specialists and generalists accumulating in old grasslands over centuries of grazing management. Habitat heterogeneity increased specialist species richness at both scales, suggesting that management policies should favour maintenance of a heterogeneous mosaic of open areas, trees and shrubs in temperate grazed grasslands. Although grassland specialists are sensitive to grassland isolation, in extensively managed landscapes with high landscape diversity input of grassland species from the landscape matrix may buffer negative effects of habitat fragmentation on grassland communities. (Less)

53 citations

Journal ArticleDOI
10 Sep 2007-Wear
TL;DR: In this article, the distribution of hardness and indentation modulus of subsurface layers was obtained through the Universal hardness indentation technique, and the key stages of MML formation were observed and presented.

53 citations


Authors

Showing all 3757 results

NameH-indexPapersCitations
James Chapman8248336468
Alexandre Alexakis6754017247
Bernard Waeber5637035335
Peter A. Andrekson5457312042
Charles S. Peirce5116711998
Lars M. Blank493018011
Fushuan Wen494659189
Mati Karelson4820710210
Ago Samoson461198807
Zebo Peng453597312
Petru Eles443006749
Vijai Kumar Gupta433016901
Eero Vasar432636930
Rik Ossenkoppele421926839
Tõnis Timmusk4110511056
Network Information
Related Institutions (5)
Norwegian University of Science and Technology
68.9K papers, 1.9M citations

88% related

Royal Institute of Technology
68.4K papers, 1.9M citations

86% related

Delft University of Technology
94.4K papers, 2.7M citations

86% related

Polytechnic University of Milan
58.4K papers, 1.2M citations

86% related

University of Ljubljana
47K papers, 1M citations

85% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202342
2022107
2021883
2020951
2019882
2018745