Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

• Jari Järvi,
• Patrick Rinke and
• Milica Todorović

Beilstein J. Nanotechnol. 2020, 11, 1577–1589, doi:10.3762/bjnano.11.140

• -06CH11357. Funding This work has received funding from the Academy of Finland via the Artificial Intelligence for Microscopic Structure Search (AIMSS) project No. 316601 and the Flagship programme: Finnish Center for Artificial Intelligence FCAI, and from the Emil Aaltonen Foundation.

Multiwalled carbon nanotube based aromatic volatile organic compound sensor: sensitivity enhancement through 1-hexadecanethiol functionalisation

• Nadra Bohli,
• Meryem Belkilani,
• Juan Casanova-Chafer,
• Eduard Llobet and
• Adnane Abdelghani

Beilstein J. Nanotechnol. 2019, 10, 2364–2373, doi:10.3762/bjnano.10.227

• selectivity. It was also shown to improve the sensor response dynamics. These results combined with previous results [22][32] could be interesting for the development of functionalised multisensor arrays combined with an artificial intelligence algorithm for selectivity enhancement. Synoptic structure of the

Wearable, stable, highly sensitive hydrogel–graphene strain sensors

• Jian Lv,
• Chuncai Kong,
• Chao Yang,
• Lu Yin,
• Itthipon Jeerapan,
• Fangzhao Pu,
• Xiaojing Zhang,
• Sen Yang and
• Zhimao Yang

Beilstein J. Nanotechnol. 2019, 10, 475–480, doi:10.3762/bjnano.10.47

• the field of bioelectronics, artificial intelligence, and soft robotics [1][2]. Among these sensors, strain sensors can translate an external applied tensile force into electrical signal, hence attracting numerous research efforts for health monitoring, biomechanics studies and artificial skin for

Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• application in the fields of artificial intelligence and machine learning [1]. The future of cellular and satellite communications, radar systems, deep sea and space exploration will likely be closely related to the capability of ANNs to provide effective solutions to problems such as classification and

An analytical approach to evaluate the performance of graphene and carbon nanotubes for NH₃ gas sensor applications

• Elnaz Akbari,
• Vijay K. Arora,
• Aria Enzevaee,
• Mohamad. T. Ahmadi,
• Mehdi Saeidmanesh,
• Mohsen Khaledian,
• Hediyeh Karimi and
• Rubiyah Yusof

Beilstein J. Nanotechnol. 2014, 5, 726–734, doi:10.3762/bjnano.5.85

• Elnaz Akbari Vijay K. Arora Aria Enzevaee Mohamad. T. Ahmadi Mehdi Saeidmanesh Mohsen Khaledian Hediyeh Karimi Rubiyah Yusof Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia Faculty of Electrical Engineering, Universiti Teknologi

Analytical development and optimization of a graphene–solution interface capacitance model

• Hediyeh Karimi,
• Rasoul Rahmani,
• Reza Mashayekhi,
• Leyla Ranjbari,
• Amir H. Shirdel,
• Niloofar Haghighian,
• Parisa Movahedi,
• Moein Hadiyan and
• Razali Ismail

Beilstein J. Nanotechnol. 2014, 5, 603–609, doi:10.3762/bjnano.5.71

• Hediyeh Karimi Rasoul Rahmani Reza Mashayekhi Leyla Ranjbari Amir H. Shirdel Niloofar Haghighian Parisa Movahedi Moein Hadiyan Razali Ismail Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia Malaysia Japan International Ins. Of Technology