People

Principal Investigator

Konstantin Sergeevich Novoselov

Title

Director

Degree

Ph.D, Radboud University of Nijmegen, 2004
MSc, Moscow Institute of Physics and Technology, 1997

Research Interests

Condensed matter physics; Mesoscopic transport, superconductivity and ferromagnetism; Nanostructures and Nanofabrication; Graphene and other two-dimensional crystals

Office Location

S9-09-02D

Biography

Professor Sir Konstantin Novoselov FRS, foreign associate of the National Academy of Sciences, USA

Prof Sir Konstantin ‘Kostya’ Novoselov FRS was born in Russia in August 1974. He is best known for isolating graphene at The University of Manchester in 2004, and is an expert in condensed matter physics, mesoscopic physics and nanotechnology. Every year since 2014 Kostya Novoselov is included in the list of the most highly cited researchers in the world. He was awarded the Nobel Prize for Physics in 2010 for his achievements with graphene. Kostya is a director of the Institute for Functional Intelligent Materials and holds a position of a Tan Chin Tuan Centennial Professor at the National University of Singapore. He is also part time Langworthy Professor of Physics and the Royal Society Research Professor at The University of Manchester.

He graduated from the Moscow Institute of Physics and Technology, and undertook his PhD studies at the University of Nijmegen in the Netherlands before moving to The University of Manchester in 2001. Later Professor Novoselov joined the National University of Singapore in 2019. Professor Novoselov has published more than 400 peer-reviewed research papers. He was awarded with numerous prizes, including Nicholas Kurti Prize (2007), International Union of Pure and Applied Science Prize (2008), MIT Technology Review young innovator (2008), Europhysics Prize (2008), Bragg Lecture Prize from the Union of Crystallography (2011), the Kohn Award Lecture (2012), Leverhulme Medal from the Royal Society (2013), Onsager medal (2014), Carbon medal (2016), Dalton medal (2016), Otto Warburg Prize (2019), John von Neumann Professor from the John von Neumann Computer Society (2022) among many others. He was knighted in 2010 as Knight Commander of the Order of the Netherlands Lion and knighted in 2012 as Knight Bachelor in the United Kingdom New Year Honours for services to science.

View Full CV here

View full list of publications here.

I-FIM Publications:

179 entries « ‹ 1 of 36 › »

2026

Yang, Kou; Bi, Xueli; Zhong, Haibin; Wang, Juncheng; Luan, Yanju; Zheng, Shushen; Andreeva, Daria; Novoselov, Konstantin; Zhang, Shanqing

Stimuli-responsive graphene oxide composites: working mechanisms, design strategies, and applications

PROGRESS IN MATERIALS SCIENCE, 158 , 2026, DOI: 10.1016/j.pmatsci.2025.101649.

Abstract | BibTeX | Endnote

Wang, Zhuyuan; Jia, Chen; Wang, Yuxiang; Yan, Xue; Yong, Ming; Low, Ze-Xian; Zeng, Xiangkang; Yang, Jindi; Zhang, Hao; Li, Xuefeng; Sun, Kaige; Tebyetekerwa, Mike; Xu, Rongming; Zhao, Wenming; Xu, Kaijie; Kang, Yuan; Strounina, Ekaterina; Andreeva, Daria V; Whittaker, Andrew K; Liu, Jefferson Zhe; Zhao, Chuan; Novoselov, Kostya S; Zhang, Xiwang

Confined polymerization in nanochannels for synthesizing functional membranes

Nature Synthesis, 5 , 2026, DOI: 10.1038/s44160-026-00991-z.

Abstract | BibTeX | Endnote

@article{Wang2026_WOS001674879800001,
title = {Confined polymerization in nanochannels for synthesizing functional membranes},
author = {Zhuyuan Wang and Chen Jia and Yuxiang Wang and Xue Yan and Ming Yong and Ze-Xian Low and Xiangkang Zeng and Jindi Yang and Hao Zhang and Xuefeng Li and Kaige Sun and Mike Tebyetekerwa and Rongming Xu and Wenming Zhao and Kaijie Xu and Yuan Kang and Ekaterina Strounina and Daria V. Andreeva and Andrew K. Whittaker and Jefferson Zhe Liu and Chuan Zhao and Kostya S. Novoselov and Xiwang Zhang},
doi = {10.1038/s44160-026-00991-z},
times_cited = {0},
issn = {2731-9697},
year = {2026},
date = {2026-02-18},
journal = {Nature Synthesis},
volume = {5},
abstract = {Synthesis of robust, functional membranes is hindered by the lack of spatial control in conventional bulk-phase reactions, which offer limited regulation over network structure and nanoscale uniformity. Here we report a nanoconfinement strategy to fabricate membranes where polymerization occurs within sub-2-nm channels that act as spatially defined reaction compartments. The nanoconfined space governs nanoscale alignment and network packing, producing high-density poly(epoxy) membranes (1.51 g cm−3), 37% denser than their non-confined analogue (1.10 g cm−3) and exceeding typical polymers. The resulting materials combine high tensile strength (119.9 MPa), flexibility (100,000 bending cycles) and broad solvent resistance, unifying properties that are difficult to achieve simultaneously. We further demonstrate that producing high-density membrane matrices facilitates selective ion transport, as shown by as-fabricated positively charged poly(ammonium) membranes, which outperform state-of-the-art counterparts in terms of mechanical strength, OH⁻ conductivity and selectivity against small neutral molecules. This work demonstrates nanomaterials as spatially confined reactors to govern polymer architecture and function, while also offering fundamental insights into structure regulation under nanoconfinement.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Zhang, Hongji; Grebenko, Artem K; Litvinov, Dmitrii; Zheng, Wenwen; Iakoubovskii, Konstantin V; Grebenchuk, Sergey Y; Makarova, Anna; Fedorov, Alexander; Starkov, Andrei; Orofeo, Carlo M; Vyalikh, Denis V; Lanza, Mario; Koperski, Maciej; Novoselov, Kostya S; Toh, Chee-tat; Ozyilmaz, Barbaros

Breaking the 2-nm Barrier in Hard Disk Drives Using Monolayer Amorphous Carbon Overcoats

ADVANCED MATERIALS, 2026, DOI: 10.1002/adma.202519149.

Abstract | BibTeX | Endnote

@article{WOS:001680918000001,
title = {Breaking the 2-nm Barrier in Hard Disk Drives Using Monolayer Amorphous
Carbon Overcoats},
author = {Hongji Zhang and Artem K Grebenko and Dmitrii Litvinov and Wenwen Zheng and Konstantin V Iakoubovskii and Sergey Y Grebenchuk and Anna Makarova and Alexander Fedorov and Andrei Starkov and Carlo M Orofeo and Denis V Vyalikh and Mario Lanza and Maciej Koperski and Kostya S Novoselov and Chee-tat Toh and Barbaros Ozyilmaz},
doi = {10.1002/adma.202519149},
times_cited = {0},
issn = {0935-9648},
year = {2026},
date = {2026-02-01},
journal = {ADVANCED MATERIALS},
publisher = {WILEY-V C H VERLAG GMBH},
address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY},
abstract = {The rapid growth of artificial intelligence (AI) has increased the
demand for large-scale data storage, making hard disk drives (HDDs)
indispensable in data centers due to their cost-effectiveness and
stability. To support AI-driven data requirements, increasing the areal
storage density is critical. However, this metric is increasingly
constrained by the carbon overcoat (COC), the essential protective layer
for magnetic media. Traditional diamond-like carbon (DLC) can no longer
fulfill the stringent demands for ultrathin coatings and high thermal
stability required by next-generation technologies like Heat-Assisted
Magnetic Recording (HAMR) and bit-patterned media. Here, we introduce
monolayer amorphous carbon (MAC) as a superior alternative. MAC is
directly grown on the heterogeneous (Fe, Pt, SiO2) HDD surface at low
temperatures (similar to 300 degrees C), achieving an uniform 0.8 nm
thickness across 2.5-inch disks. Despite its atomic thickness, MAC
demonstrates high corrosion resistance and low roughness comparable to
commercial 2.5 nm COCs. Its fully amorphous, sp2-hybridized structure
ensures excellent thermal stability under HAMR-like conditions (similar
to 450 degrees C) and a low friction coefficient, enabling potential
lubricant-free operation. Replacing traditional COCs with MAC
facilitates the development of HDD media capable of achieving 10 Tb/in2,
addressing the urgent storage demands of the digital era.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

FNClarivate Analytics Web of Science
VR1.0
PTJ
AFHongji Zhang
Artem K Grebenko
Dmitrii Litvinov
Wenwen Zheng
Konstantin V Iakoubovskii
Sergey Y Grebenchuk
Anna Makarova
Alexander Fedorov
Andrei Starkov
Carlo M Orofeo
Denis V Vyalikh
Mario Lanza
Maciej Koperski
Kostya S Novoselov
Chee-tat Toh
Barbaros Ozyilmaz
TIBreaking the 2-nm Barrier in Hard Disk Drives Using Monolayer Amorphous
Carbon Overcoats
SOADVANCED MATERIALS
DTArticle
ABThe rapid growth of artificial intelligence (AI) has increased the
demand for large-scale data storage, making hard disk drives (HDDs)
indispensable in data centers due to their cost-effectiveness and
stability. To support AI-driven data requirements, increasing the areal
storage density is critical. However, this metric is increasingly
constrained by the carbon overcoat (COC), the essential protective layer
for magnetic media. Traditional diamond-like carbon (DLC) can no longer
fulfill the stringent demands for ultrathin coatings and high thermal
stability required by next-generation technologies like Heat-Assisted
Magnetic Recording (HAMR) and bit-patterned media. Here, we introduce
monolayer amorphous carbon (MAC) as a superior alternative. MAC is
directly grown on the heterogeneous (Fe, Pt, SiO2) HDD surface at low
temperatures (similar to 300 degrees C), achieving an uniform 0.8 nm
thickness across 2.5-inch disks. Despite its atomic thickness, MAC
demonstrates high corrosion resistance and low roughness comparable to
commercial 2.5 nm COCs. Its fully amorphous, sp2-hybridized structure
ensures excellent thermal stability under HAMR-like conditions (similar
to 450 degrees C) and a low friction coefficient, enabling potential
lubricant-free operation. Replacing traditional COCs with MAC
facilitates the development of HDD media capable of achieving 10 Tb/in2,
addressing the urgent storage demands of the digital era.
Z90
PUWILEY-V C H VERLAG GMBH
PAPOSTFACH 101161, 69451 WEINHEIM, GERMANY
SN0935-9648
DI10.1002/adma.202519149
UTWOS:001680918000001
ER
EF

Pant, Prabhat; Bhatt, Diksha; Rawat, Kundan Singh; Sati, Satish Chandra; Judeh, Zaher; Mahfouz, Remi; Tayeb, Talah; Qari, Nada; Andreeva, Daria V; Novoselov, Kostya S; Sahoo, Nanda Gopal

A highly efficient electrode material based on waste plastic derived rGO decorated with polypyrrole and zinc oxide nanoparticles for supercapacitor applications

JOURNAL OF MATERIALS CHEMISTRY A, 14 (8), pp. 4564-4581, 2026, DOI: 10.1039/d5ta07562a.

Abstract | BibTeX | Endnote

@article{WOS:001643929300001,
title = {A highly efficient electrode material based on waste plastic derived rGO
decorated with polypyrrole and zinc oxide nanoparticles for
supercapacitor applications},
author = {Prabhat Pant and Diksha Bhatt and Kundan Singh Rawat and Satish Chandra Sati and Zaher Judeh and Remi Mahfouz and Talah Tayeb and Nada Qari and Daria V Andreeva and Kostya S Novoselov and Nanda Gopal Sahoo},
doi = {10.1039/d5ta07562a},
times_cited = {0},
issn = {2050-7488},
year = {2026},
date = {2026-02-01},
journal = {JOURNAL OF MATERIALS CHEMISTRY A},
volume = {14},
number = {8},
pages = {4564-4581},
publisher = {ROYAL SOC CHEMISTRY},
address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND},
abstract = {The rising concern over non-biodegradable plastics, along with the
global energy issue, necessitates new and sustainable solutions. One
viable method is to convert plastic trash into value-added materials for
electrochemical applications. In this study, we describe a two-step
catalytic pyrolysis approach for producing reduced graphene oxide (rGO)
from waste plastics. Additionally, we illustrate the fabrication of a
waste plastic derived rGO-based ternary composite with ZnO and PPy,
WP-rGO/ZnO/PPy (RPZ), which improves its electrochemical performance by
providing a variety of active sites. The ternary RPZ composite's
properties were evaluated by XRD, Raman spectroscopy, FT-IR, SEM, and
XPS whereas the electrochemical efficiency was examined by CV, GCD, and
EIS. The ternary RPZ composite electrodes exhibit a remarkable specific
capacitance value of 676.4 F g-1 at 0.5 A g-1 and a strong capacitance
retention of 90.6% over 5000 cycles. Moreover, the assembled symmetric
energy storage device achieved an energy density of 27.23 Wh kg-1 at a
high-power density of 499.97 W kg-1. Notably, LED light can be powered
for up to 10 minutes using two symmetric devices that use a ternary RPZ
composite as a very effective electrode material in a series
configuration. This study contributes to the development of
environmentally friendly energy storage systems by highlighting the
potential of repurposing waste materials for advanced energy
applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

FNClarivate Analytics Web of Science
VR1.0
PTJ
AFPrabhat Pant
Diksha Bhatt
Kundan Singh Rawat
Satish Chandra Sati
Zaher Judeh
Remi Mahfouz
Talah Tayeb
Nada Qari
Daria V Andreeva
Kostya S Novoselov
Nanda Gopal Sahoo
TIA highly efficient electrode material based on waste plastic derived rGO
decorated with polypyrrole and zinc oxide nanoparticles for
supercapacitor applications
SOJOURNAL OF MATERIALS CHEMISTRY A
DTArticle
ABThe rising concern over non-biodegradable plastics, along with the
global energy issue, necessitates new and sustainable solutions. One
viable method is to convert plastic trash into value-added materials for
electrochemical applications. In this study, we describe a two-step
catalytic pyrolysis approach for producing reduced graphene oxide (rGO)
from waste plastics. Additionally, we illustrate the fabrication of a
waste plastic derived rGO-based ternary composite with ZnO and PPy,
WP-rGO/ZnO/PPy (RPZ), which improves its electrochemical performance by
providing a variety of active sites. The ternary RPZ composite's
properties were evaluated by XRD, Raman spectroscopy, FT-IR, SEM, and
XPS whereas the electrochemical efficiency was examined by CV, GCD, and
EIS. The ternary RPZ composite electrodes exhibit a remarkable specific
capacitance value of 676.4 F g-1 at 0.5 A g-1 and a strong capacitance
retention of 90.6% over 5000 cycles. Moreover, the assembled symmetric
energy storage device achieved an energy density of 27.23 Wh kg-1 at a
high-power density of 499.97 W kg-1. Notably, LED light can be powered
for up to 10 minutes using two symmetric devices that use a ternary RPZ
composite as a very effective electrode material in a series
configuration. This study contributes to the development of
environmentally friendly energy storage systems by highlighting the
potential of repurposing waste materials for advanced energy
applications.
Z90
PUROYAL SOC CHEMISTRY
PATHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND
SN2050-7488
VL14
BP4564
EP4581
DI10.1039/d5ta07562a
UTWOS:001643929300001
ER
EF

Klimova, Liudmila A; Kruglov, Ivan A; Ermolaev, Georgy A; Grudinin, Dmitriy; Arsenin, Aleksey; Volkov, Valentyn S; Novoselov, Kostya S

Materials Informatics Framework for Accelerated Discovery of High-Refractive-Index 2D Materials

ACS NANO, 20 (3), pp. 2623-2632, 2026, DOI: 10.1021/acsnano.5c10644.

Abstract | BibTeX | Endnote

@article{WOS:001661080600001,
title = {Materials Informatics Framework for Accelerated Discovery of
High-Refractive-Index 2D Materials},
author = {Liudmila A Klimova and Ivan A Kruglov and Georgy A Ermolaev and Dmitriy Grudinin and Aleksey Arsenin and Valentyn S Volkov and Kostya S Novoselov},
doi = {10.1021/acsnano.5c10644},
times_cited = {0},
issn = {1936-0851},
year = {2026},
date = {2026-01-01},
journal = {ACS NANO},
volume = {20},
number = {3},
pages = {2623-2632},
publisher = {AMER CHEMICAL SOC},
address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
abstract = {Accurate and efficient prediction of the optical properties of
two-dimensional (2D) materials is crucial for photonic applications but
remains a challenging task due to discrepancies between theoretical and
experimental approaches. Here, we present a physics-guided machine
learning (ML) framework for accelerated screening of 2D materials. It
combines first-principles density functional theory (DFT) calculations
and graph neural network models backed by experimental spectroscopic
validation and Cauchy-model integration. Within this framework, we
collected a database of more than 1000 monolayers of transition metal
dichalcogenides (TMDs) along with their optical properties. We also
proposed a universal method for defining a physically meaningful
thickness of two-dimensional structures, which enabled a correction of
the optical properties obtained from PBE-based density functional
theory. Using the collected database, we developed a Cauchy-model-based
machine learning model to calculate refractive indices in the
near-infrared (near-IR) region (755-1064 nm). The developed approach
reflects correlations between the atomic structures of monolayers and
their optical properties, which are confirmed by extensive testing
against independent 2D materials databases. In this way, our ML-driven
strategy offers a powerful tool for the rapid screening of novel
monolayer materials with tailored optical functionalities, significantly
accelerating the discovery and design of next-generation photonic
materials. As an application, we further demonstrate how
high-refractive-index candidates such as Bi2Te2Se enable enhanced field
confinement and long crosstalk lengths in monolayer waveguides,
highlighting their promise for integrated photonics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

FNClarivate Analytics Web of Science
VR1.0
PTJ
AFLiudmila A Klimova
Ivan A Kruglov
Georgy A Ermolaev
Dmitriy Grudinin
Aleksey Arsenin
Valentyn S Volkov
Kostya S Novoselov
TIMaterials Informatics Framework for Accelerated Discovery of
High-Refractive-Index 2D Materials
SOACS NANO
DTArticle
ABAccurate and efficient prediction of the optical properties of
two-dimensional (2D) materials is crucial for photonic applications but
remains a challenging task due to discrepancies between theoretical and
experimental approaches. Here, we present a physics-guided machine
learning (ML) framework for accelerated screening of 2D materials. It
combines first-principles density functional theory (DFT) calculations
and graph neural network models backed by experimental spectroscopic
validation and Cauchy-model integration. Within this framework, we
collected a database of more than 1000 monolayers of transition metal
dichalcogenides (TMDs) along with their optical properties. We also
proposed a universal method for defining a physically meaningful
thickness of two-dimensional structures, which enabled a correction of
the optical properties obtained from PBE-based density functional
theory. Using the collected database, we developed a Cauchy-model-based
machine learning model to calculate refractive indices in the
near-infrared (near-IR) region (755-1064 nm). The developed approach
reflects correlations between the atomic structures of monolayers and
their optical properties, which are confirmed by extensive testing
against independent 2D materials databases. In this way, our ML-driven
strategy offers a powerful tool for the rapid screening of novel
monolayer materials with tailored optical functionalities, significantly
accelerating the discovery and design of next-generation photonic
materials. As an application, we further demonstrate how
high-refractive-index candidates such as Bi2Te2Se enable enhanced field
confinement and long crosstalk lengths in monolayer waveguides,
highlighting their promise for integrated photonics.
Z90
PUAMER CHEMICAL SOC
PA1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN1936-0851
VL20
BP2623
EP2632
DI10.1021/acsnano.5c10644
UTWOS:001661080600001
ER
EF

179 entries « ‹ 1 of 36 › »