Library Reader Issue 04: The eBook Edge

Library resource awareness poster covering electronic books, Kill A Wat energy meters, and Staff Pick Rome.https://dune.une.edu/libraryreader/1003/thumbnail.jp

Non-volatile resistive switching mechanism in single-layer MoS2 memristors:insights from ab initio modelling of Au and MoS2 interfaces

Non-volatile memristive devices based on two-dimensional (2D) layered materials provide an attractive alternative to conventional flash memory chips. Single-layer semiconductors, such as monolayer molybdenum disulphide (ML-MoS 2), enable the aggressive downscaling of devices towards greater system integration density. The "atomristor", the most compact device to date, has been shown to undergo a resistive switching between its high-resistance (HRS) and low-resistance (LRS) states of several orders of magnitude. The main hypothesis behind its working mechanism relies on the migration of sulphur vacancies in the proximity of the metal contact during device operation, thus inducing the variation of the Schottky barrier at the metal-semiconductor interface. However, the interface physics is not yet fully understood: other hypotheses were proposed, involving the migration of metal atoms from the electrode. In this work, we aim to elucidate the mechanism of the resistive switching in the atomristor. We carry out density functional theory (DFT) simulations on model Au and ML-MoS 2 interfaces with and without the presence of point defects, either vacancies or substitutions. To construct realistic interfaces, we combine DFT with Green's function surface simulations. Our findings reveal that it is not the mere presence of S vacancies but rather the migration of Au atoms from the electrode to MoS 2 that modulate the interface barrier. Indeed, Au atoms act as conductive "bridges", thus facilitating the flow of charge between the two materials. </p

A Mixed-Signal Oscillatory Neural Network for Scalable Analog Computations in Phase Domain

Digital electronics based on von Neumann's architecture are reaching their limits to solve large scale problems essentially due to the memory fetching. Instead, recent efforts to bring the memory near the computation have enabled highly parallel computations at low energy cost. Oscillatory Neural Network (ONN) is one example of in-memory analog computing paradigm consisting of coupled oscillating neurons. When implemented in hardware, ONNs naturally perform gradient descent of an energy landscape that makes them particularly suited for solving optimization problems. Although the ONN computational capability and its link with the Ising model are known for decades, implementing a large-scale ONN remains difficult. Beyond the oscillators' variations, there are still design challenges such as having compact, programmable synapses and a modular architecture for solving large problem instances. In this paper, we propose a mixed-signal architecture named Saturated Kuramoto ONN (SKONN) that leverages both analog and digital domains for efficient ONN hardware implementation. SKONN computes in the analog phase domain while propagating the information digitally to facilitate scaling up the ONN size. SKONN's separation between computation and propagation enhances the robustness and enables a feed-forward phase propagation that is showcased for the first time. Moreover, the SKONN architecture leads to unique binarizing dynamics that are particularly suitable for solving NP-hard combinatorial optimization problems such as finding the Weighted Max-cut of a graph. We find that SKONN's accuracy is as good as the Goemans-Williamson 0.878-approximation algorithm for Max-cut; whereas SKONN's computation time only grows logarithmically. We report on Weighted Max-cut experiments using a 9-neuron SKONN proof-of-concept on PCB. Finally, we present a low-power 16-neuron SKONN integrated circuit and illustrate SKONN's feed-forward ability while computing the XOR function

How fast can vanadium dioxide neuron-mimicking devices oscillate? Physical mechanisms limiting the frequency of vanadium dioxide oscillators

The frequency of vanadium dioxide (VO2) oscillators is a fundamental figure of merit for the realization of neuromorphic circuits called oscillatory neural networks (ONNs) since the high frequency of oscillators ensures low-power consuming, real-time computing ONNs. In this study, we perform electrothermal 3D technology computer-aided design (TCAD) simulations of a VO2 relaxation oscillator. We find that there exists an upper limit to its operating frequency, where such a limit is not predicted from a purely circuital model of the VO2 oscillator. We investigate the intrinsic physical mechanisms that give rise to this upper limit. Our TCAD simulations show that it arises a dependence on the frequency of the points of the curve current versus voltage across the VO2 device corresponding to the insulator-to-metal transition (IMT) and metal-to-insulator transition (MIT) during oscillation, below some threshold values of $C_{\mathrm{ext}}$. This implies that the condition for the self-oscillatory regime may be satisfied by a given load-line in the low-frequency range but no longer at higher frequencies, with consequent suppression of oscillations. We note that this variation of the IMT/MIT points below some threshold values of $C_{\mathrm{ext}}$ is due to a combination of different factors: intermediate resistive states achievable by VO2 channel and the interplay between frequency and heat transfer rate. Although the upper limit on the frequency that we extract is linked to the specific VO2 device we simulate, our findings apply qualitatively to any VO2 oscillator. Overall, our study elucidates the link between electrical and thermal behavior in VO2 devices that sets a constraint on the upper values of the operating frequency of any VO2 oscillator

Anxiety, depression and worries in advanced Parkinson Disease during COVID-19 pandemic

Background The psychological impact of the COVID-19 outbreak and lockdown on frail populations with advanced Parkinson disease (APD) and their caregivers may present with peculiar features and require specific interventions. Methods We enrolled here 100 APD patients and 60 caregivers. Seventy-four patients were treated with device-aided therapies (DAT) and 26 with standard medical treatment (SMT). Through a telephonic interview, subjects underwent the Hospital Anxiety and Depression Scale (HADS-A; HADS-D), and an ad hoc questionnaire to explore thoughts and emotions related to the pandemic. Results Depression was observed in 35% of APD patients and anxiety in 39%, with a significant reduction of the latter after the lockdown (p= 0.023). We found a significant correlation between the type of therapy and the HADS-A score (p= 0.004). Patients’ main worries were as follows: a possible higher risk of COVID-19 infection (25%), interruption of non-pharmacological treatments (35%), interruption of outpatient clinics (38%), PD complications related to COVID-19 (47%). Patients treated with DAT manifested worries about device-related issues and risk for caregivers’ infection. The 40% of caregivers showed anxiety, while the 21.7% of them showed depression. Conclusion Our study reveals a higher prevalence of anxiety and the presence of peculiar worries and needs in APD patients during the pandemic alongside psychological sequelae of their caregivers. These findings are important for neurologists and healthcare services to foster strategies for the management of psychological distress in both patients and caregivers

Large-scale quantum chemistry simulations of organic photovoltaics

Organic photovoltaic (OPV) devices rely on the mixture between a conjugated copolymer (electron donor) and an electron acceptor material (typically, but not necessarily, functionalised fullerenes): this active layer is known as the bulk heterojunction, and it is crucial for the device operation, as this is where excitons are split into free electrons and holes to produce current. A deep understanding of the role of the molecular structure of materials on the device physics is necessary to achieve better performances, and to this end, computer simulations are undoubtedly a powerful tool. However, the bulk heterojunction is a complex system, and for theoretical models to be representative, these should be composed of thousands of atoms, a size out of the reach of atomistic quantum mechanics simulations. To overcome this limitation, this project involved the use of the ONETEP code, which, thanks to its linear-scaling computational cost with respect to the system size, allowed to carry out ab initio calculations, within the density functional theory (DFT) framework, on OPV materials and models of bulk heterojunctions on a far larger scale than possible before. Regardless of the device morphology and architecture, fundamental for the exciton splitting is the energy level alignment of the donor and the acceptor components, and several ways exist to fine-tune the electronic properties of these materials. Nevertheless, is it possible to find novel and alternative routes to the well-known strategies currently employed in the laboratory? As for the donor polymer, the results here presented suggest that acting on the polymerisation statistics, that is, the ratio of different blocks in the polymer chain, significantly affects the electronic structure of such materials, with changes in the band gap of the same order of magnitude of those induced by the widely used functionalisation approach. The acceptor fullerenes, on the iii other hand, are generally more challenging to functionalise, and consequently their electronic structure cannot be trivially tuned. However, one could ideally circumvent the issue via the intercalation of different solvent molecules in the crystal phase of fullerenes, in order to attempt to indirectly modify the energy of the frontier orbitals and the band gap. Indeed, results highlighted the crucial role of the solvent in modifying both the electronic and the optical properties of solid-state fullerenes through the formation of fullerene-solvent ⇡-⇡ interactions, which disrupt the close packing of solvent-free fullerenes. Interestingly, more appreciable changes were observed in the properties of pure rather than functionalised fullerenes. Another, yet fundamental, aspect of OPV explored here is the polymeracceptor interaction in the bulk heterojunction, with the acceptor material consisting of fullerene and the more recently introduced non-fullerene acceptors (NFAs). Although attempts to model the polymer-fullerene interface to investigate its excited-state properties are numerous, these have been limited to, within the framework of ab initio atomistic simulations, small 1-to-1 short-oligomer-fullerene pair models. For the first time, DFT calculations for ground and excited state were performed on model interfaces of realistic size composed of more than a single polymer chain and dozens of fullerene molecules, allowing to gain new insights into the physics of exciton generation and splitting. For instance, it was observed that the probability of charge transfer to occur is deeply influenced by the polymer block statistics, and that exciton dissociation is favoured by large polymer phases rather than large fullerene phases, although these are still beneficial. Evidence of long-range charge-transfer states in the low-energy part of the excited-state spectrum was also observed. On the other hand, models of polymer-NFA interfaces are still scarce in the literature, as NFA phases are more intrinsically complex to model than fullerene phases. Critical for both charge mobility and device performance is the NFA solid-state arrangement with respect to the polymer. By constructing large polymer-NFA model interfaces it was possible to highlight and confirm the importance of intermolecular ⇡-⇡ stacking interactions in the NFA phase, as it was found that these deeply influence the exciton delocalisation, the exciton splitting rate, and the mobility anisotropy. This work, which is the outcome of collaboration with Merck, was enabled by the linear-scaling capabilities of the ONETEP code, which allowed to study large-scale realistic models of OPV. This thesis provided novel and important insights into different aspects of organic photovoltaics, both in terms of material design and device physics

Dataset for: Large-Scale Quantum Chemistry Simulations of Organic Photovoltaics

This dataset supports the thesis entitled &#39;Large-Scale Quantum Chemistry Simulations of Organic Photovoltaics by Boschetto.</span

Capillary-force-driven self-assembly of carbon nanotubes: from ab initio calculations to modeling of self-assembly

International audienceIn this paper, density functional theory and theory of contacts and adhesion of fibrillar interfaces (Gecko’s effect) are combined to investigate elasto-capillary-driven self-assembly of carbon nanotubes arrays

First Principles Simulations of MoS2 Towards the Non-Enzymatic Sensing of Cortisol

International audienceAtomically thin two-dimensional (2D) materials have been-and are still currently beingextensively studied due to their unique mechanical, electrical, and optical properties, which, together with their ultra-thin size, enable the development of compact devices and innovative technologies. Within the vast chemical space of transition metal dichalcogenides (TMDs), single-layer molybdenum disulphide (MoS2) is with no doubt one of the most studied material due to its stability and its direct optical band gap of 1.8 eV, which make it the ideal candidate to be used in a wide range of nanoelectronic devices, going beyond conventional CMOS technology. Here we look at MoS2 in the context of biosensing, and we study such material as the core component of field-effect biosensors (Bio-FETs) for the detection of cortisol. Ultimately, the aim of this study is to design and integrate such biosensors in wearable health monitoring devices. We want to bridge the gap between materials' properties and device physics and to do so, we carry out first-principles atomistic computer simulations in the framework of density functional theory (DFT). Our study constitutes the first step of a wider multi-scale modelling approach in which the goal is to construct a full atomistic-to-device level model. Recently, MoS2 has been studied as a sensing platform for detecting mainly gas and small biological molecules, such as glucose. [3] Enzymatic biosensing is the most common approach, however, non-enzymatic sensing can provide higher sensor stability and prompt response at the expense of chemical selectivity. Here, we are interested in the non-enzymatic detection of cortisol in human sweat as a mean to monitor the risk of cardiovascular diseases. However, the mechanisms that govern the interaction between the analyte and MoS2 at the molecular level are far from being understood. Thus, we thoroughly explore the MoS2/cortisol interaction in terms of both structural, electronic, and charge transfer properties to assess viable sensing mechanisms. We study the impact of some of the most used metal dopants employed in lab-scale experiments, such as Ni, Pt, Pd, in order to modulate the sensing platform with respect to bare MoS2. In addition to single-atom doping, we also explore the use of metal nanoparticles (e.g., Pt and Au) to decorate the MoS2 layer as yet another mean to detect cortisol. Overall, our work ultimately aims to obtain a deep understanding of the properties of MoS2 when used as a sensor to drive the design of devices towards better performance

Investigating MoS2 as the Sensing Substrate for the Non-Enzymatic Detection of Cortisol via Quantum Mechanical DFT Simulations

International audienceAtomically thin two-dimensional (2D) materials have been —and are still currently being— extensively studied due to their unique mechanical, electrical, and optical properties, which, together with their ultra- thin size, enable the development of compact devices and innovative technologies. Within the vast chemical space of transition metal dichalcogenides (TMDs), single-layer molybdenum disulphide (MoS2) is with no doubt one of the most studied material due to its stability and its direct optical band gap of 1.8 eV, which make it the ideal candidate to be used in a wide range of nanoelectronic devices, going beyond conventional CMOS technology. [1]Here we look at MoS2 in the context of biosensing, and we study such material as the core component of field-effect biosensors (Bio-FETs) for the detection of cortisol. Ultimately, the aim of this study is to design and integrate such biosensors in wearable health monitoring devices. [2] We want to bridge the gap between materials’ properties and device physics and to do so, we carry out first-principles atomistic computer simulations in the framework of density functional theory (DFT). Our study constitutes the first step of a wider multi-scale modelling approach in which the goal is to construct a full atomistic-to-device level model.Recently, MoS2 has been studied as a sensing platform for detecting mainly gas and small biological molecules, such as glucose. [3] Enzymatic biosensing is the most common approach, however, non- enzymatic sensing can provide higher sensor stability and prompt response, at the expense of chemical selectivity. Here, we are interested in the non-enzymatic detection of cortisol in human sweat as a mean to monitor the risk of cardiovascular diseases. However, the mechanisms that govern the interaction between the analyte and MoS2 at the molecular level are far from being understood. Thus, we thoroughly explore the MoS2/cortisol interaction in terms of both structural, electronic, and charge transfer properties to assess viable sensing mechanisms. We study the impact of some of the most used metal dopants employed in lab-scale experiments, such as Ni, Pt, Pd, in order to modulate the sensing platform with respect to bare MoS2. In addition to single-atom doping, we also explore the use of metal nanocluster (e.g., Pt and Au) to decorate the MoS2 layer as yet another mean to detect cortisol.Overall, our work ultimately aims to obtain a deep understanding of the properties of MoS2 when used as a sensor to drive the design of devices towards better performance