research highlights
MOLECULAR MACHINES

A cog in the machine

Angew. Chem. Int. Ed. 55, 14628–14632 (2016)
HeatingCoolingtemperature change, the pyridine groups
facilitate an intra-carboxyl proton shuttle,
resulting in rotation of the azodipyridine
and a correlated translation of the tetra-acid.
In other words, rotary motion is converted
to linear motion. Density functional theory
calculations suggested that ionization ofthe carboxyl groups kinetically aids theazodipyridine pedalling motion. This
thermally-induced process involves a single
crystal to single crystal phase transition.
Propagation of the motion through thenetwork causes a macroscopic expansion
and contraction of the crystal of around1.82 mm (123–333 K).BLB
NANOMATERIALS

Covalent welding
WILEY

Nano Lett. 16, 7282–7289 (2016)

Proton transport is ubiquitous in biology —for example, through the hydrogen-bonded
networks of protein channels. Whilesome synthetic proton shuttles have beenrealized, such systems have rarely been used
as a switching mechanism in functional
materials. Now, Osamu Sato and colleagues
at Kyushu University and the Institute forMaterials Science in Japan have designedsupramolecular organic frameworks (SOFs)
that contract and expand at the macroscale
on intramolecular proton shuttling.
The researchers first prepared a
SOF from an azodipyridine and a tetracarboxylic acid. These two groups arehydrogen-bonded to one another to forma rhombic grid and act as the basis of arack-and-pinion (gear) cascade. On a

Carbon-based materials have been rapidly
adopted in real-world applications.
However, many of their outstanding
electrical and mechanical properties only
occur at the nanoscale, which imposes
constraints on the design of macrosized devices. In order to explore thepotential of carbon nanomaterials, 2D
building blocks have to be assembled
into 3D nanostructures, ideally withoutcompromising the nanoscale performance.
Liangbing Hu and co-workers at
the University of Maryland have now
proposed a route to bulk materials withenhanced electrical conductivity andtensile strength from carbon nanofibre
networks. In the pristine matrix, amorphousinterweaved nanofibres interact with
one another via weak van der Waals
forces. High temperature Joule heating

NANOPARTICLESMoved aroundSci. Adv. 2, e1601462 (2016)

Biological processes such as encapsulation, recognition and transportation are complex. Many
methods have been used to translate the efficiency and specificity of these processes into
simple and smart functional materials. Now, inspired by the way cells engulf debris and repair
damage in the body, researchers in the US have created polymer-based droplets that canrecognize, pick up and drop off hydroxyapatite nanoparticles from surfaces.
Hydroxyapatite — the main component in bone — is rich in calcium. To create a droplet thatwill bind to hydroxyapatite, Todd Emrick and co-workers at the University of Massachusetts
functionalized phosphorylcholine-polyolefin with catechol and then shook an aqueous solutionof this polymer in an organic solvent. When pumped through a flow cell over hydroxyapatitecoated substrates under laminar flow, the polymer-stabilized oil-in-water emulsion droplets
were able to pick up the hydroxyapatite nanoparticles. Pick-up efficiency was lower under basic
conditions and in solutions containing calcium ions. Furthermore, substrate composition also
affected pick-up: greater efficiency was seen with PDMS and mica over plastic films. Exploiting
this substrate-dependent pick-up efficiency, Emrick and colleagues showed that nanoparticles
picked up from silicon substrates can be dropped off downstream on a polydopamine-coatedsilicon substrate. Such surface-to-surface nanoparticle transport is useful for transferring
nanoparticle properties between materials. ALC

induced by ultrafast electric current pulses
enables the gradual merging of adjacentcarbon nanofibres where they intersect.
The heating effect also leads to further
carbonization of the nanofibres, and theresulting 3D matrix has high crystallinity
and strong chemical and physical bonding.
Compared to the amorphous nanofibres,
the electrical conductivity of the covalently
interconnected carbon network was morethan four orders of magnitude higher.
Interestingly, the same conditions didn’t
prove successful when applied to othercarbon-based nanostructures. In highly
crystalline carbon nanotubes, Joule heating
resulted in deformation of the strong
C–C bonds and overall deterioration of
their electronic properties.
OB
NEUROMORPHIC COMPUTATION

Clever analog memristors
Front. Neurosci. 10, 482 (2016)

The electrical resistance state of a memristor
is a function of the history of currents andvoltages previously applied to the device.
Such behaviour closely resembles theactivity-dependent plasticity of biologicalsynapses, making memristors natural
candidates to implement learning processes
in neuromorphic hardware architectures. In
analog memristors the possible resistancestates are multi-valued, offering a closer
analogy to biological synapses. However, theimplementation of unsupervised learning
processes based on networks of analog
memristors has been rarely reported.
Now, Erika Covi and colleagues at
the National Research Council in Italy
and the University of Southampton
in the UK have reported a networkof 125 analog memristors capableof reproducing unsupervised
learning tasks. Each memristor is a
TiN/HfO2/TiN heterostructure and theresearchers exploited filamentary resistive
switching processes to mimic the synaptic
plasticity therein. The memristor network
connects two layers of artificial neurons —the so-called pre- and post-neurons. A 5 × 5square array of pre-neurons, each of which
act as a black or white pixel, provides imagesof letters as input signals to the network.
The first activated post-neuron defines
the output signal. After an unsupervised
training process, the network was able toassociate one specific post-neuron to eachdifferent letter being shown, demonstrating
successful recognition. This was also verified
for noisy or incomplete input images.
GP
Written by Bryden Le Bailly, Olga Bubnova,
Ai Lin Chun and Giacomo Prando.

NATURE NANOTECHNOLOGY | VOL 11 | DECEMBER 2016 | www.nature.com/naturenanotechnology

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