scispace - formally typeset
Search or ask a question
Institution

Delft University of Technology

EducationDelft, Zuid-Holland, Netherlands
About: Delft University of Technology is a education organization based out in Delft, Zuid-Holland, Netherlands. It is known for research contribution in the topics: Population & Catalysis. The organization has 37681 authors who have published 94404 publications receiving 2741710 citations. The organization is also known as: TU-Delft & Technische Hogeschool Delft.


Papers
More filters
Proceedings ArticleDOI
05 Nov 2003
TL;DR: T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks, introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it to handle load variations in time and location.
Abstract: In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an activesleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput.We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.

2,844 citations

Journal ArticleDOI
01 Jan 1998-Nature
TL;DR: In this paper, the results of scanning tunnelling microscopy and spectroscopy on individual single-walled nanotubes from which atomically resolved images allow us to examine electronic properties as afunction of tube diameter and wrapping angle.
Abstract: Carbon nanotubes can be thought of as graphitic sheets with a hexagonal lattice that have been wrapped up into a seamless cylinder. Since their discovery in 19911, the peculiar electronic properties of these structures have attracted much attention. Their electronic conductivity, for example, has been predicted2,3,4 to depend sensitively on tube diameter and wrapping angle (a measure of the helicity of the tube lattice), with only slight differences in these parameters causing a shift from a metallic to a semiconducting state. In other words, similarly shaped molecules consisting of only one element (carbon) may have very different electronic behaviour. Although the electronic properties of multi-walled and single-walled nanotubes5,6,7,8,9,10,11,12 have been probed experimentally, it has not yet been possible to relate these observations to the corresponding structure. Here we present the results of scanning tunnelling microscopy and spectroscopy on individual single-walled nanotubes from which atomically resolved images allow us to examine electronic properties as afunction of tube diameter and wrapping angle. We observe bothmetallic and semiconducting carbon nanotubes and find thatthe electronic properties indeed depend sensitively on thewrapping angle. The bandgaps of both tube types are consistent with theoretical predictions. We also observe van Hove singularities at the onset of one-dimensional energy bands, confirming the strongly one-dimensional nature of conduction within nanotubes.

2,797 citations

Journal ArticleDOI
TL;DR: The Support Vector Data Description (SVDD) is presented which obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions.
Abstract: Data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the Support Vector Data Description (SVDD) which is inspired by the Support Vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the Support Vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the Support Vector Data Descriptions using artificial and real data.

2,789 citations

Journal ArticleDOI
03 Apr 1997-Nature
TL;DR: In this article, electrical transport measurements on individual single-wall nanotubes have been performed to confirm the theoretical predictions of single-walled nanotube quantum wires, and they have been shown to act as genuine quantum wires.
Abstract: Carbon nanotubes have been regarded since their discovery1 as potential molecular quantum wires. In the case of multi-wall nanotubes, where many tubes are arranged in a coaxial fashion, the electrical properties of individual tubes have been shown to vary strongly from tube to tube2,3, and to be characterized by disorder and localization4. Single-wall nanotubes5,6 (SWNTs) have recently been obtained with high yields and structural uniformity7. Particular varieties of these highly symmetric structures have been predicted to be metallic, with electrical conduction occurring through only two electronic modes8–10. Because of the structural symmetry and stiffness of SWNTs, their molecular wavefunctions may extend over the entire tube. Here we report electrical transport measurements on individual single-wall nanotubes that confirm these theoretical predictions. We find that SWNTs indeed act as genuine quantum wires. Electrical conduction seems to occur through well separated, discrete electron states that are quantum-mechanically coherent over long distance, that is at least from contact to contact (140nm). Data in a magnetic field indicate shifting of these states due to the Zeeman effect.

2,678 citations

Journal ArticleDOI
09 Nov 2001-Science
TL;DR: This work demonstrates logic circuits with field-effect transistors based on single carbon nanotubes that exhibit a range of digital logic operations, such as an inverter, a logic NOR, a static random-access memory cell, and an ac ring oscillator.
Abstract: We demonstrate logic circuits with field-effect transistors based on single carbon nanotubes. Our device layout features local gates that provide excellent capacitive coupling between the gate and nanotube, enabling strong electrostatic doping of the nanotube from p-doping to n-doping and the study of the nonconventional long-range screening of charge along the one-dimensional nanotubes. The transistors show favorable device characteristics such as high gain (>10), a large on-off ratio (>10(5)), and room-temperature operation. Importantly, the local-gate layout allows for integration of multiple devices on a single chip. Indeed, we demonstrate one-, two-, and three-transistor circuits that exhibit a range of digital logic operations, such as an inverter, a logic NOR, a static random-access memory cell, and an ac ring oscillator.

2,642 citations


Authors

Showing all 38152 results

NameH-indexPapersCitations
Albert Hofman2672530321405
Charles M. Lieber165521132811
Ad Bax13848697112
George C. Schatz137115594910
Georgios B. Giannakis137132173517
Jaap S. Sinninghe Damsté13472661947
Avelino Corma134104989095
Mark A. Ratner12796868132
Jing Kong12655372354
Robert J. Cava125104271819
Reza Malekzadeh118900139272
Jinde Cao117143057881
Mike S. M. Jetten11748852356
Liquan Chen11168944229
Oscar H. Franco11182266649
Network Information
Related Institutions (5)
Georgia Institute of Technology
119K papers, 4.6M citations

95% related

École Polytechnique Fédérale de Lausanne
98.2K papers, 4.3M citations

94% related

Technical University of Denmark
66.3K papers, 2.4M citations

94% related

ETH Zurich
122.4K papers, 5.1M citations

94% related

Hong Kong University of Science and Technology
52.4K papers, 1.9M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023393
2022784
20215,396
20205,525
20195,229