Long-range selective transport of anions and cations in graphene oxide membranes, causing selective crystallization on the macroscale

Monoatomic nanosheets can form 2-dimensional channels with tunable chemical properties, for ion storage and filtering applications. Here, we demonstrate transport of K+, Na+, and Li+ cations and F- and Cl- anions on the centimeter scale in graphene oxide membranes (GOMs), triggered by an electric bias. Besides ion transport, the GOM channels foster also the aggregation of the selected ions in salt crystals, whose composition is not the same as that of the pristine salt present in solution, highlighting the difference between the chemical environment in the 2D channels and in bulk solutions

A robust, modular approach to produce graphene-MO X multilayer foams as electrodes for Li-ion batteries

Major breakthroughs in batteries would require the development of new composite electrode materials, with a precisely controlled nanoscale architecture. However, composites used for energy storage are typically a disordered bulk mixture of different materials, or simple coatings of one material onto another. We demonstrate here a new technique to create complex hierarchical electrodes made of multilayers of vertically aligned nanowalls of hematite (Fe 2 O 3 ) alternated with horizontal spacers of reduced graphene oxide (RGO), all deposited on a 3D, conductive graphene foam. The RGO nanosheets act as porous spacers, current collectors and protection against delamination of the hematite. The multilayer composite, formed by up to 7 different layers, can be used with no further processing as an anode in Li-ion batteries, with a specific capacity of up to 1175 μA h cm -2 and a capacity retention of 84% after 1000 cycles. Our coating strategy gives improved cyclability and rate capacity compared to conventional bulk materials. Our production method is ideally suited to assemble an arbitrary number of organic-inorganic materials in an arbitrary number of layers

Real-time imaging of Na+ reversible intercalation in "Janus" graphene stacks for battery applications

Sodium, in contrast to other metals, cannot intercalate in graphite, hindering the use of this cheap, abundant element in rechargeable batteries. Here, we report a nanometric graphite-like anode for Na+ storage, formed by stacked graphene sheets functionalized only on one side, termed Janus graphene. The asymmetric functionalization allows reversible intercalation of Na+, as monitored by operando Raman spectroelectrochemistry and visualized by imaging ellipsometry. Our Janus graphene has uniform pore size, controllable functionalization density, and few edges; it can store Na+ differently from graphite and stacked graphene. Density functional theory calculations demonstrate that Na+ preferably rests close to -NH2 group forming synergic ionic bonds to graphene, making the interaction process energetically favorable. The estimated sodium storage up to C6.9Na is comparable to graphite for standard lithium ion batteries. Given such encouraging Na+ reversible intercalation behavior, our approach provides a way to design carbon-based materials for sodium ion batteries

Measurement of the conformational switching of azobenzenes from the macro- to attomolar scale in self-assembled 2D and 3D nanostructures

It is important, but challenging, to measure the (photo)induced switching of molecules in different chemical environments, from solution through thin layers to solid bulk crystals. We compare the cis-trans conformational switching of commercial azobenzene molecules in different liquid and solid environments: polar solutions, liquid polymers, 2D nanostructures and 3D crystals. We achieve this goal by using complementary techniques: optical absorption spectroscopy, femtosecond transient absorption spectroscopy, Kelvin probe force microscopy and reflectance spectroscopy, supported by density functional theory calculations. We could observe the same molecule showing fast switching in a few picoseconds, when studied as an isolated molecule in water, or slow switching in tens of minutes, when assembled in 3D crystals. It is worth noting that we could also observe switching for small ensembles of molecules (a few attomoles), representing an intermediate case between single molecules and bulk structures. This was achieved using Kelvin probe force microscopy to monitor the change of surface potential of nanometric thin 2D islands containing ca. 10(6) molecules each, self-assembled on a substrate. This approach is not limited to azobenzenes, but can be used to observe molecular switching in isolated ensembles of molecules or other nano-objects and to study synergistic molecular processes at the nanoscale

A robust, modular approach to produce graphene–MOx multilayer foams as electrodes for Li-ion batteries

Major breakthroughs in batteries would require the development of new composite electrode materials,with a precisely controlled nanoscale architecture. However, composites used for energy storage are typi-cally a disordered bulk mixture of different materials, or simple coatings of one material onto another. Wedemonstrate here a new technique to create complex hierarchical electrodes made of multilayers of verti-cally aligned nanowalls of hematite (Fe2O3) alternated with horizontal spacers of reduced graphene oxide(RGO), all deposited on a 3D, conductive graphene foam. The RGO nanosheets act as porous spacers,current collectors and protection against delamination of the hematite. The multilayer composite, formedby up to 7 different layers, can be used with no further processing as an anode in Li-ion batteries, with aspecific capacity of up to 1175μAhcm−2and a capacity retention of 84% after 1000 cycles. Our coatingstrategy gives improved cyclability and rate capacity compared to conventional bulk materials. Our pro-duction method is ideally suited to assemble an arbitrary number of organic–inorganic materials in anarbitrary number of layers

Trasporto ionico selettivo in membrane a base grafene per applicazioni in sensoristica e biomedicali

Le proprietà di selettività ionica e direzionalità dei canali ionici biologici sono studiate da decenni e continuano a essere fonti di ispirazione per la realizzazione di materiali e strutture per applicazioni nella sensoristica e nel campo biomedicale. Nell’ultimo decennio, grande attenzione è stata rivolta allo studio delle membrane a base grafene ossido (GO) che permettono di realizzare materiali e sistemi che mimano alcune proprietà dei canali ionici per applicazioni tecnologiche. In genere, l’integrazione di materiali che mimano alcuni processi biologici con sistemi a stato solido offre un enorme interesse per riprodurre le funzioni e i principi dei canali ionici biologici. Il GO è un materiale bidimensionale composto da un singolo foglio monoatomico di grafene fortemente ossidato (concentrazione di ossigeno 30% in peso). La presenza di numerosi gruppi funzionali, quali idrossili, ossidrili, carbossili, permette di ottenere sospensioni stabili di GO in tutti i comuni solventi. In particolare, il GO forma sospensioni acquose stabili per un paio di anni. Tale caratteristica permette una elevata processabilità del GO che può essere riassemblato formando strutture 2D/3D in maniera controllata. La struttura assemblata più semplice è la membrana lamellare, dove tutti i singoli fogli sono paralleli con una distanza inferiore a 2 nanometri e che può essere finemente controllata in funzione dell’umidità residua dell’aria. Tali membrane mostrano un potenziale applicativo in numerosi settori quali: la desalinizzazione e purificazione dell’acqua, la separazione di ioni e gas, biosensoristica, conduttori protonici, sistemi per batterie e super-capacitori. La combinazione di tali membrane con piccole molecole organiche (PAH) permette di creare una nuova classe di materiali lamellari con proprietà fotochimiche controllabili. L’obiettivo di questa tesi di dottorato è la realizzazione di sistemi compositi lamellari GO-PAH. Questa tesi rappresenta il primo passo per lo sviluppo di tali compositi. Per questo motivo i materiali vengono studiati separatamente per poi essere miscelati. I PAH utilizzati appartengono a una nota classe di molecole fotocromiche quali le azobenzeni, la cui isomerizzazione cis- e trans- può essere pilotata tramite stimoli luminosi e il loro comportamento in diversi ambienti (liquido, superficie, incapsulato in matrici polimeriche), nonché confinati in membrane lamellari di GO. Per quanto riguarda le membrane lamellari di GO, un quadro complessivo dei meccanismi responsabili della selettività ionica non è ancora stato sviluppato, perciò il lavoro di questa tesi si prefigge di chiarire alcuni meccanismi, nonché di realizzare dispositivi sensoristici. Per questo motivo sono state progettate e fabbricate membrane autoportanti di GO incapsulate in matrici polimeriche per lo studio del trasporto ionico e successivamente integrate in dispositivi stampati per biosensori. La funzionalizzazione con PAH permette di modificare e controllare la capacità di riconoscimento e trasporto selettivi del sistema composito. Le membrane a base di GO così ottenute mostrano una notevole selettività ionica analoga a quella dei canali di ioni biologici. La combinazione delle membrane a base GO con elettrodi commerciali (ChemSens) hanno permesso lo sviluppo di un biosensore indossabile che misuri la concentrazione di K+/Na+ nel sudore e che può essere utilizzato per monitorare malattie quali la ipopotassiemia e la fibrosi cistica nei bambini.Biological ion channels intelligently controlling ions across cell membranes serve as a big source of bio-inspiration for the scientists to build bio-inspired smart solid-state nanopores and nanochannels with practical applications. Graphene oxide (GO) based membranes that differentiate ions are being actively developed to meet the needs in separation, sensing, biomedical, and water treatment technologies. Biomimetic approaches that combine bioinspired functional molecules with solid state supports offer great potential for imitating the functions and principles of biological ion channels. GO is an atomic-thick sheet of carbon atoms with oxygen (content 30% in weight). The presence of different functional groups, such as epoxides, alcohols, and carboxylic acids, allows to obtain stable dispersion in all of the most common solvents and water, in particular (stability > 2years). The high processability of GO in water allows to use it as building block to realise 2D/3D structures with tuned order. The simplest structure is the lamellar membrane where all the GO sheets are stacked with a distance lower than 2 nm. Moreover, the distance can be easily tuned with humidity. These membranes have shown potential in a variety of applications, including water desalination and purification, gas and ion separation, biosensors, proton conductors, lithium-based batteries and super-capacitors. The combination of 2D materials with small organic molecules (PAH) allows to create new composite materials that merge together the 2-dimensional structure of graphene-based materials with the tunable (photo)chemical properties of dyes. This thesis is the first step to develop GO-PAH composites. For this reason, the two materials are separately investigated and after mixed. Regarding PAH, we study the behaviour of a well-known class of photoswitching molecules (azobenzenes) from cis to trans isomer using light stimuli as an ideal probe to study conformational freedom in different constrained environments and, furthermore, confined in lamellar GO membranes. Regarding the GO membranes, a comprehensive understanding of all the mechanisms involved in the ion selectivity has not been developed. This work aims to clarify some aspects and as well as the design and fabrication of biomimetic and free-standing graphene oxide (GO) based membranes covered with polymer that can be integrated into a biosensing 3D printed device. GO functionalization with PAH allows to tune the capabilities for selective recognition and transport of the composite membranes. The resulting GO-based membranes show remarkable ion selectivity toward the specific ion of interest, for the transport across the membranes as in the biological ion channels. By combining the selective graphene based membranes with commercial electrodes (ChemSens) we develop a wearable and selective biosensor for K+/Na+ in sweat that can be used to monitor disease such as hypokalemia or cystic fibrosis in children

Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(Ⅲ)/Co(Ⅱ) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R) value of 0.9655 and mean square error value of 6.77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis

Membrane based In-situ reduction of graphene oxide for electrochemical supercapacitor application

Reduced graphene oxide (rGO) is a widely studied electrode material for energy storage, however, its strong re-stacking tendency during chemical reduction always leads to a degraded specific surface area and thus limits its performance. Therefore, it is necessary to control the morphology of rGO during the reduction process. Here, we develop a novel in-situ membrane-based method for the reduction of graphene oxide (GO) using a green and efficient vitamin C (VC) aqueous solution as reductant. The obtained electrode material (vitamin C reduced GO via membrane-based method, VG-M) exhibits a specific capacitance of 174 F/g at 1 A/g and 75.9% of retention at 40 A/g, which is about 9 times better than the highly self-stacked material from conventional methods (vitamin C reduced GO via stirring method, VG-S). This designed method successfully achieves the maintenance of rGO sheet morphology through laminar confinement in GO membrane and presents a simple approach towards two-dimensional (2D) material morphology control

Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(Ⅲ)/Co(Ⅱ) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R2) value of 0.9655 and mean square error value of 6.77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis.Dali Ji acknowledges UNSW Tuition Fee Scholarship and Australian Research Council Discovery Project DP180101436. Vanesa Quintano acknowledges the funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska Curie Grant Agreement No. 101066462.Peer reviewe