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Animal behavior is a complex interaction between physiology and psychology. Yet, most artificial intelligence (AI) systems do not take into account psychological factors in their decision-making. One obvious reason for this exclusion is the lack of comprehensive understanding of the connection between physiology and psychology at the neuronal level. However, recent advances in brain imaging and molecular and genetic tools have revealed that there are specific neural circuits in the brain through which physiology and psychology are hardwired for regulating animal behaviors such as feeding. Developing neuro-mimetic circuits that can integrate the influence of both physiology and psychology can enable a new emotional-AI paradigm that can bridge the gap between humans and machines. Here we demonstrate, for the first time, a bio-inspired gustatory circuit that can mimic adaptive feeding behavior for humans based on both the physiological states of the body such as hunger, and the psychological state of the mind such as appetite. For our demonstration, we use graphene-based chemitransistors as artificial gustatory taste receptor neurons to design an “electronic tongue” and monolayer MoS2 based memtransistors to design an “electronic gustatory cortex” that include physiology-drive “hunger neuron”, psychology-driven “appetite neuron” and a “feeding circuit”. We also show adaptive feeding behavior by exploiting the analog and non-volatile programming capability of the MoS2 memtransistors. We believe that our demonstration can institute a new paradigm for emotional neuromorphic systems and at the same time have widespread consequences for human health. The concept of gustatory emotional intelligence introduced in this work can also be translated to other sensory systems including visual, audio, tactile, and olfactory emotional intelligence to aid future humanoid AI.

The development of highly selective and miniaturized chemisensors for liquid media can enable newer discoveries and empower a wide range of disciplines from biology to human health, marine ecology to climate change, and industrial process monitoring to food safety. While the two-terminal chemiresistor architecture has been remarkably successful in this regard, they rely on appropriate surface functionalization to achieve selectivity for a specific target analyte. Furthermore, sensor drift and sensor-to-sensor variation in a multiplexed chemisensors array can severely impact their reliability and obscure the response of individual sensors to the target analytes. In this article, we overcome these challenges by introducing a new sensing paradigm that integrates an array of three-terminal chemitransistors based on monolayer graphene with an ensemble of machine learning (ML) algorithms. The extraordinary transport properties of graphene combined with its electrochemically inert basal plane allow a wide range of chemical species to electrostatically control the channel conductivity in a liquid medium through voltage gating. This in turn expands the parameter space for the sensing elements which enables the integration of various unsupervised and supervised ML algorithms to eliminate the need for surface functionalization, eradicate the need for sensor calibration, and minimize the impact of sensor-to-sensor variation. Our ML-aided, non-functionalized, and reusable graphene chemitransistors were found to be highly selective to de-ionized (DI) water, multiple chloride salts including NaCl, LiCl, CaCl2, and MgCl2 that are relevant to biosensing, several weak acids including acetic, propionic, and citric acid that are used in the food industry, as well as more complex solutions and processes such as decaying juices extracted from different fruits (grape, orange, watermelon, and pineapple).

Detecting a potential collision at night is a challenging task owing to the lack of discernible features that can be extracted from the available visual stimuli. To alert the driver or, alternatively, the maneuvering system of an autonomous vehicle, current technologies utilize resource draining and expensive solutions such as light detection and ranging (LiDAR) or image sensors coupled with extensive software running sophisticated algorithms. In contrast, insects perform the same task of collision detection with frugal neural resources. Even though the general architecture of separate sensing and processing modules is the same in insects and in image-sensor-based collision detectors, task-specific obstacle avoidance algorithms allow insects to reap substantial benefits in terms of size and energy. Here, we show that insect-inspired collision detection algorithms, when implemented in conjunction with in-sensor processing and enabled by innovative optoelectronic integrated circuits based on atomically thin and photosensitive memtransistor technology, can greatly simplify collision detection at night. The proposed collision detector eliminates the need for image capture and image processing yet demonstrates timely escape responses for cars on collision courses under various real-life scenarios at night. The collision detector also has a small footprint of ∼40 μm

In this work, we have examined and proposed a dielectrically modulated biosensor based on the dual trench transparent gate engineered MOSFET (DM DT GE-MOSFET) for label-free detection of biomolecules with enhanced sensitivity and efficiency. Different sensing parameters such as the ION/IOFF, threshold voltage shift have been evaluated to validate the sensing metric for the proposed device. Additionally, the SVth (Vth Sensitivity) has been also analyzed by considering the charged (positive and negative) biomolecules. In addition to this, the RF sensing parameters such as the transconductance gain and cut-off frequency have been also taken into account to provide a better insight into the sensitivity analysis of the proposed device. Furthermore, the linearity, distortion and noise immunity of the device has been evaluated to check the overall performance of the biosensor at high frequency (GHz). Moreover, the results indicate that, the proposed biosensor exhibits a SVth of 0.68 for the positively charged biomolecules at a very low drain bias (0.2 V). Therefore, the proposed device can be used as an alternative to the conventional FET-based biosensors.

The dielectrically modulated heterostructure TFET based nanocavity embedded label-free biosensors are emerging as low power, highly sensitive bio-analyte detectors. High sensitivity and fast detection of biomolecules are still a challenge for researchers. In this article, single cavity dual-material extended gate heterostructure (III-V) TFET (SC-DM-EG HTFET) based dielectrically modulated label-free biosensor is proposed; which promises higher sensitivity and better device performances such as, ON current, $\text{I}_{ON}/\text{I}_{OFF}$ ratio, subthreshold swing (SS); compared with single cavity dual-material heterostructure TFET (SC-DM HTFET), dual cavity dual-material heterostructure TFET (DC-DM HTFET), as well as, previously proposed FET based biosensors. 2D numerical simulation of the biosensors was performed with SILVACO ATLAS 2D simulation software. III-V heterostructure (InGaAs/Si) and extended gate geometry provide increased tunneling probability, improved gate control, high $\text{I}_{ON}/\text{I}_{OFF}$ ratio, and ultra-high sensitivity, compared to IV-IV heterostructure biosensors. The sensitivities of the biosensors are analyzed for both neutral and charged biomolecules, with dielectric constants $\text{K}=5$ ,7,10,12. Effect of non-ideal issues on sensitivity, such as temperature fluctuation, steric hindrance are also studied for the biosensors mentioned above. Benchmarking is done to provide a quantitative comparison of the proposed biosensor with published literature. A maximum sensitivity of $1.3\times 10^{8}$ , along with the $\text{I}_{ON}/\text{I}_{OFF}$ ratio of $2\times 10^{12}$ and SS of 25.4 mV/V is noticed in SC-DM-EG HTFET for the dielectric constant of $\text{K}=12$ in a completely filled cavity of neutral biomolecules.

The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS

Dielectric modulated (DM) field-effect transistors (FET) have gained significant popularity for label-free detection of biomolecules. However, the inherent short channel effects limit their sensitivity, scalability and energy-efficiency. Therefore, to realize the true potential of the DMFET based biosensors, in this work, we propose a highly scalable, extremely sensitive and energy-efficient DM nanotube tunnel FET (NT-TFET) biosensor for label-free detection of biomolecules by modifying the structure of the conventional NT-TFET. The modified architecture facilitates the realization of a nanocavity at the source-channel tunneling junction and also provides stability to the immobilized biomolecules. We have performed an extensive analysis of the performance of the proposed DM NT-TFET biosensor in the presence of different representative target biomolecules characterized by different dielectric constants, and/or ionized charge densities using calibrated TCAD simulations. Our results indicate that the proposed DM NT-TFET exhibits an extremely high threshold voltage sensitivity (SVth) of 0.44, a high selectivity exceeding four orders of magnitude, ON-state current sensitivity (SION) of more than five orders of magnitude and could be a promising alternative to the conventional FET based dielectric modulated biosensors. Moreover, the sensitivity of the proposed DM NT-TFET could be further improved by utilizing alternate source materials with lower bandgap or by probing the transient response of the drain current and exploiting the difference in the settling time for different biomolecules with different dielectric constant (κ).

Collision detection under poor illumination and nightly conditions pose significant challenge for manned and unmanned vehicles, flying drones, and robots navigating complex terrestrial and extraterrestrial geographies. Existing night vision cameras offer solution based on sophisticated enhancement algorithms or additional thermal sensors necessitating extensive and expensive hardware, which make them power-hungry and untenable for deployment in remote and resource constrained locations. In contrast, nocturnal flying insects can avoid collision using very limited neural resources. Insect-inspired collision detectors based on silicon complementary metal oxide semiconductor technology and field programmable gate arrays also exist, however, the physical separation between sensing and compute and absence of spike-based information processing capability increases their area and energy overhead. Here, we introduce an insect-inspired, spike-based, and in-sensor collision detector using a reconfigurable optoelectronic integrated circuit constructed based on atomically-thin and light-sensitive memtransistors. We imitate the escape response of lobula giant movement detector (LGMD) neuron found in many insect species and demonstrate timely collision detection for various real-life scenarios at night involving cars on collision course. Our collision detector has a small effective footprint of 40 µm2 and consumes miniscule energy of few hundreds pico-Joules.

Aims:: In this work, a Junction-Less Double Gate MOSFET (JLDG MOSFET) based CMOS inverter circuit is proposed for ultra-low power applications in the near and sub-threshold regime operations. Background:: D.C. performances like power, delay and voltage swing of the proposed Inverter have been modeled analytically and analyzed in depth. JLDG MOSFET has promising features to reduce the short-channel effects compared to the planner MOSFET because of better gate control mechanism. So, proposed Inverter would be efficacious to offer less power dissipation and higher speed. Objective:: Impact of supply voltage, temperature, High-k gate oxide, TOX, TSI on the power, delay and voltage swing of the Inverter circuits have been detailed here. Methods: Extensive simulations using SILVACO ATLAS have been done to validate the proposed logic based digital circuits. Besides, the optimum supply voltage has been modelled and verified through simulation for low voltage operations. In depth analysis of voltage swing is added to measure the noise immunity of the proposed logic based circuits in Sub & Near-threshold operations. For ultra-low power operation, JLDG MOSFET can be an alternative compared to conventional planar MOSFET. Result:: Hence, the analytical model of delay, power dissipation and voltage swing have been proposed of the proposed logic based circuits. Besides, the ultra-low power JLDG CMOS inverter can be an alternative in saving energy, reduction of power consumption for RFID circuit design where the frequency range is a dominant factor. Conclusion:: The power consumption can be lowered in case of UHF, HF etc. RF circuits using the Double Gate Junction-less MOSFET as a device for circuit design.

In this article, the electrical behavior of short channel SiGe Heterostructure Junction-Less DG-MOSFET have been studied by incorporating the quantum mechanical effect and short channel effects. Analytical model and simulation result shows how the device process parameters like gate oxide thickness, silicon thickness, channel doping concentration, channel or gate length etc. have an impact on the D.C parameters like threshold voltage, surface potential. DIBL and threshold voltage of the nanoscale JL-MOSFET are also analyzed by considering the QME to raise the accuracy of the derived models. Extensive simulations are performed in SILVACO ATLAS TCAD tool to validate the proposed models. It is quite evident that the derived models and simulation results are in good agreement for a wide variation of process parameters. Schrodinger model has been used in ATLAS simulation platform to validate the derived analytical model considering QME. However, comprehensive analysis of the short channel SiGe Hetero-structure Junction-Less Double Gate Metal Oxide Semiconductor Field Effect Transistor gives a better view and understanding of D.C characteristics of the device. At the end, D.C characteristics (power dissipation, propagation delay, power-delay product) of an inverter circuit has been taken under consideration to check the impact of QMEs on circuit performance.

Analytical model for the computation of drain current of the junction-less double gate junction-less MOSFET with a variable barrier height has been presented in this paper. Band non-parabolicity is also assumed to increase the efficacy of the proposed model, applicable for the ultrathin nano-devices. Variable barrier height uses intra band tunnelling to enhance the ION/IOFF by reducing the off current of the device significantly. Also low sub-threshold slope can be achieved using this proposed device structure for high switching applications. Besides ten stages Inverter has been implemented using the proposed device to study the impact of constrictions on power dissipations and delay. The proposed models have been validated by the extensive simulations using SILVACO TCAD. Simulation shows that the proposed data is matched with the simulated data with high accuracy.

A customized distributed security system has been designed by using RFID to achieve a reliable high-end communication system for universities. In this work, an overview of the existing problems rel...

In this paper we investigate a junction-less dual-gate metal–oxide–semiconductor field effect transistor (JL-DG-MOSFET)-based programmable inverter with an oxide–nitride–oxide (SiO2/Si3N4/SiO2) gate stack, which offers short-/long-term memory as well as logic functionalities depending on charge trapping in the nitride layer. It has been shown that the pulsing interval plays a pivotal role in deciding the short-term plasticity/long-term plasticity window based on the charges trapped/detrapped at/near the oxide–nitride interface. Moreover, we have demonstrated a JL-DG-MOSFET-based complementary metal–oxide–semiconducor inverter with a programmable switching threshold and propose a scheme for secure key generation for authentication. The intra-Hamming distance among the 21 keys generated by the programmable inverter is also depicted to demonstrate the efficacy of the proposed framework. This will eliminate the physical separation between the logic and memory and can offer attractive solutions for silicon-based low-power neuromorphic computing and hardware security.

In this article, a highly sensitive and efficient label free biosensor based on dielectric modulation has been evaluated by considering the extended gate engineering mechanism along with the SiGe/Si heterostructure (H) tunnel (T) FET architecture. Moreover, the 2D simulations have been executed using SILVACO ATLAS TCAD tool. Besides, the proposed HTFET provides stability to the immobilized biomolecules within the sensing cavity because of the extended gate at the source/channel interface. The proposed SiGe/Si HTFET device shows impressive performance in terms of sensitivity against the variation of different neutral, positively and negatively charged biomolecules. Our results depict that the proposed device is capable of providing an On Current Sensitivity (SION) of 7.5 x 109 for k = 12. Therefore, the DM-GE-HTFET (Dual Metal Gate Engineered HTFET) can be used as an alternative to the conventional TFET based biosensors.

In this article, the sensitivity and selectivity of a Si-Ge Hetero-structure Junction-less Double Gate MOSFET (DG-MOSFET) based biosensor has been analyzed by introducing the self-heating issue, when the bio-particles get trapped inside the underlap or cavity region of the sensor. Sensitivity of the proposed device has been analyzed in terms of threshold voltage variation by considering the dielectric modulation method and also by considering the temperature variation. However, the prime focus is to accurately select the temperature range to avoid the performance degradation due to the self-heating issue. Simulation results are obtained by using SILVACO ATLAS tool. So, change in threshold voltage of the device has been considered as the sensing element to study the existence of bio-particles being trapped in the cavity region of the device. Sub-threshold operation has been considered here while analyzing the sensor performance.

A customized and highly secured EVM system has been designed by using RFID (Sengupta et al in Eur J Sci Res 97(4):592–603 (2013) [1]) to achieve a reliable high-end communication system for the purpose of election and voting. In this work, an overview of the existing problems related to the electronic voting machine has been introduced initially and then the proposed systems are designed using RFID (Sengupta et al in Eur J Sci Res 97(4):592–603 (2013) [1]), (Bag and Sarkar in Int J Radio Freq Ident Technol Appl 4(2):197–211 (2013) [2]) in detail. The problems related to security breeching at the voting centers are very common. A detailed study has been done considering the security breaching and access in case of the complete voting system using EVM. Keeping in mind, the present scenario of high-end securities for the corporate industries, banks, etc., we have designed a secure and flexible RFID-based EVM system using low frequency RFID technology (Bag and Sarkar in Int J Radio Freq Ident Technol Appl 4(2):197–211 (2013) [2]) and biometric sensors. The system has been practically implemented in laboratory, and the experimental data shows that this system can be widely used by the election commission at the voting centers to maintain the confidentiality.

This article demonstrates a simulation based analysis of sensitivity parameter of AlxGa1-xAs/GaAs Junction-less Double Gate MOSFET (DG-MOSFET) in the form of a biosensor by considering the Nano-Gap Filling and Self-Heating issue. In this work, a Nano-Gap has been introduced in the gate oxide region which acts as a cavity for trapping the bio-particles or biomolecules. Also, the sensitivity of the biosensor has been taken under consideration by incorporating the dielectric modulation method. Hence, the complete performance of the device has been evaluated by introducing the Nano-Gap Filling factor and Temperature Variation. Simulations have been performed extensively by using SILVACO ATLAS TCAD tool. Threshold Voltage change or AVTH is used as the sensitivity parameter, which shows highest sensitivity in case of 100% (Fully Filled) filled Nano-Gap at a low voltage (VDs=0.2V). Thus, the addressed issues will help in the realization of biosensors for early detection of diseases.

In this proposed article, a realization of RFID memory cell has been performed using Dual Material Double Gate Stack-Oxide Junction-Less MOSFET for high speed and low power application [1] in Sub-threshold regime. SNM, Power and Delay of the Memory Cell or SRAM circuit in different operating modes have been analyzed in depth. Dual Material Double Gate Oxide-Stack Junction-Less MOSFET (DMDGS-JLT) shows promising $\mathrm{I}_\mathrm{ON}/\mathrm{I}_\mathrm{OFF}$ ratio, less subthreshold swing and less Drain Induced Barrier Lowering or DIBL, in comparison with Double Gate Junction-Less MOSFET. So, proposed SRAM cell would be efficacious to offer less power dissipation and higher speed and a better Static Noise Margin. The impact of DMDGS-JLT in realizing RFID memory cell or SRAM has been studied in sub-threshold regime for ultra-low power tag design. Extensive simulations are performed using SILVACO ATLAS platform to validate the analyzed models. Besides, an optimum supply voltage range has been chosen to get an ultra-low power and higher speed of operation. DMDGS-JLT can be an alternative for ultra-low power Passive-RFID tag design, which results into greater time-span of the battery.

This work presents the analysis of a charge pump circuit for UHF RFID tag design using 22nm CMOS technology. A charge pump circuit is basically a DC to DC converter. This circuit can be used in many systems for its high performance and low power consumption. Hence, this voltage charge pump circuit can be used in low voltage applications such as in RFID tag's EEPROM. This charge pump circuit has been used as a part of the power supply unit of a fully integrated RFID transponder IC. This modified circuit can generate a stable output voltage with low power dissipation and higher gain for RFID applications. Measured output of this circuit at 433MHz frequency with 1pF of pumping capacitor value, is 3.48V which is more than the value of the industry standardized voltage 3.3V. The extensive simulations are done by using T-spice simulator.

In this work, a simple Junction-Less Double Gate MOSFET (JL-DGMOS) based Source-Coupled Logic (SCL) inverter circuit is proposed for low power applications in the near and sub-threshold regime. D.C performances like power and delay have been analyzed in depth. JLDG MOSFET have promising advantages over conventional MOSFET to mitigate the short channel effects because of better gate control mechanism. So, the proposed SCL Inverter would be efficacious to offer less power dissipation and less delay. Impact of supply voltage and frequency on the power and delay of the Inverter circuits have been analyzed here. Extensive simulations are done using SILVACO ATLAS to validate the proposed models. Optimization of the process parameters and the supply voltage has been shown to enhance the efficiency.

In this paper, a Junction-Less Double Gate MOSFET (JLDG MOSFET) based CMOS inverter circuit is proposed for ultra-low power applications in the near and sub-threshold regime operations. D.C performances like power and delay of the proposed Inverter have been modeled analytically and analyzed in depth. JLDG MOSFET has promising features to reduce the short channel effects compared to the planner MOSFET because of better gate control mechanism. So, proposed Inverter would be efficacious to offer less power dissipation and higher speed. Impact of supply voltage and the temperature on the power and delay of the Inverter circuits have been detailed here. Extensive simulations have been done using SILVACO ATLAS to validate the proposed models. Besides, optimum, supply voltage has been proposed to enhance the efficiency at low supply voltage.

Porous silica structures with intricate design patterns form the exoskeleton of diatoms, a large class of microscopic mineralized algae, whose structural features have been observed to exist down to nanoscale dimensions. Nanoscale patterned porous silica structures have also been manufactured for the use in optical systems, catalysts, and semiconductor nanolithography. The mechanical properties of these porous structures at the nanoscale are a subject of great interest for potential technological and biomimetic applications in the context of new classes of multifunctional materials. Previous studies have established the emergence of enhanced toughness and ductility in nanoporous crystalline silica structures over bulk silica. The authors undertake molecular dynamics simulations and theoretical size-scaling studies of elasticity and strength of a simple model of generic nanoporous silica structures, used to establish a theoretical model for the detailed mechanisms behind their improved properties, and show...

We analyze the fracture of single crystal silicon simulated by atomistic modeling with ReaxFF first principles based reactive force field. The simulations are performed at three temperatures: 500 K, 800 K and 1200 K, capturing both brittle and ductile behavior for the selected crystallographic orientation with (1 0 0) as the fracture plane. Three failure mechanisms are observed: bond breaking, amorphization and emission of dislocations. We demonstrate that the Crack Tip Opening Displacement (CTOD) gives a realistic estimate of the fracture toughness of brittle fracture, linking continuum mechanics fracture theory with the direct crack tip atomistic approach. We discuss the physics based mechanisms of failure in silicon in view of the CTOD measurements.

We model a single crystal of silicon approaching dimensions of 20 nm × 20 nm × 10 nm with up to 200,000 atoms based on the ReaxFF first principles based reactive force field, applied here to examine the influence of crack tip heterogeneities on the nucleation of dislocations from crack tips as the temperature is increased well beyond 1000 K. Our study aims at generating insight into mechanisms of the brittle-to-ductile transition (BDT) observed in silicon, which is known to change the behavior of silicon crystals from predominantly brittle at low temperatures to ductile at higher temperatures, likely involving a competition between brittle bond-breaking and emission of dislocations. We first examine the crystallographic orientations most widely known to exhibit fracture, for example failure on the {1 1 1} and {1 1 0} planes. We find that even at very high temperatures considered here (around 1500 K), the material behaved in a completely brittle manner for systems with cracks oriented in the {1 1 1} and {1 1 0} planes. In contrast, when the crack plane is changed to the {1 0 0} orientation, the formation of complete dislocation loops is observed. The results reported in our paper is the first direct observation of dislocation loops in silicon driven by an increase in the temperature, and extends earlier studies that were focused solely on extremely thin quasi-two-dimensional models. We demonstrate here that the formation of these loops can be linked to the geometrical ledges at the crack tip with favorable orientations for slip on the {1 1 1} planes. The detailed mechanisms leading to the nucleation of dislocations involve changes at the crack tip with bond rotations and formation of ledges, and we find that the emergence of dislocations causes the crack to arrest, but re-initiation takes place and no conclusive nucleation of dislocations and complete arrest of the crack is observed. The thickness effect is discussed together with other sources for formation of ledges along the crack front. Our findings provide important insight towards the development of models for the brittle–ductile-transition in silicon and potentially other materials, and emphasize on the significance of thickness effects in simulations of fracture in crystal slabs.

A universal design paradigm in biology is the use of hierarchies, which is evident in the structure of proteins, cells, tissues, and organisms, as well as outside the material realm in the design of signaling networks in complex organs such as the brain. A fascinating example of a biological structure is that of diatoms, a microscopic mineralized algae that is mainly composed of amorphous silica, which features a hierarchical structure that ranges from the nano- to the macroscale. Here, we use the porous structure found at submicron length scales in diatom algae as a basis to study a bioinspired nanoporous material implemented in crystalline silica. We consider the mechanical performance of two nanoscale levels of hierarchy, studying an array of thin-walled foil silica structures and a hierarchical arrangement of foil elements into a porous silica mesh structure. By comparing their elastic, plastic, and failure mechanisms under tensile deformation, we elucidate the impact of hierarchies and the wall width of constituting silica foils on the mechanical properties, by carrying out a series of large-scale molecular dynamics (MD) simulations with the first principles based reactive force field ReaxFF. We find that by controlling the wall width and by increasing the level of hierarchy of the nanostructure from a foil to a mesh, it is possible to significantly enhance the mechanical response of the material, creating a highly deformable, strong, and extremely tough material that can be stretched in excess of 100 pct strain, in stark contrast to the characteristic brittle performance of bulk silica. We find that concurrent mechanisms of shearing and crack arrest lead to an enhanced toughness and are enabled through the hierarchical assembly of foil elements into a mesh structure, which could not be achieved in foil structures alone. Our results demonstrate that including higher levels of hierarchy are beneficial in improving the mechanical properties and deformability of intrinsically brittle materials. The findings reported here provide insight into general material design approaches that may enable us to transform a brittle material such as silicon or silica into a ductile, yet strong and tough material, solely through alterations of its structural arrangement at the nanoscale.

Structural hierarchies are universal design paradigms of biological materials, e.g., several materials in nature used for carrying mechanical load or impact protection such as bone, nacre, dentin show structural design at multiple length scales from the nanoscale to the macroscale. Another example is the case of diatoms, microscopic mineralized algae with intricately patterned silica-based exoskeletons, with substructure from the nanometer to micrometer length scale. Previous studies on silica nano-honeycomb structures inspired from these diatom substructures at the nanoscale have shown a great improvement in plasticity, ductility and toughness through these designs over macroscopic silica, though along with a substantial reduction in stiffness. Here, we extend the study of these structural designs to the micron length scale by introducing additional hierarchy levels to implement a multilevel composite design. To facilitate our computational experiments we first develop a mesoscale particle-spring model description of the mechanics of bulk silica/nano-honeycomb silica composites. Our mesoscale description is directly derived from constitutive material behavior found through atomistic simulations at the nanoscale with the first principles-based ReaxFF force field, but is capable of describing deformation and failure of silica materials at tens of micrometer length scales. We create several models of randomly-dispersed fiber-composite materials with a small volume fraction of the nano-honeycomb phase, and analyze the fracture mechanics using J-integral and R-curve studies. Our simulations show a dominance of quasi-brittle fracture behavior in all cases considered. For particular materials with a small volume fraction of the nano-honeycomb phase dispersed as fibers within a bulk silica matrix, we find a large improvement (≈4.4 times) in toughness over bulk silica, while retaining the high stiffness (to 70%) of the material. The increase in toughness is observed to arise primarily from crack path deflection and crack bridging by the nano-honeycomb fibers. The first structural hierarchy at the nanometer scale (nano-honeycomb silica) provides large improvements in ductility and toughness at the cost of a large reduction in stiffness. The second structural hierarchy at the micron length scale (bulk silica/nano-honeycomb composite) recovers the stiffness of bulk silica while substantially improving its toughness. The results reported here provide direct evidence that structural hierarchies present a powerful design paradigm to obtain heightened levels of stiffness and toughness from multiscale engineering a single brittle — and by itself a functionally inferior material — without the need to introduce organic (e.g., protein) phases. Our model sets the stage for the direct simulation of multiple hierarchical levels to describe deformation and failure of complex biological composites.

Graphene Nanocutting Through Nanopatterned Vacancy Defects Rhonda Jack1 2, Dipanjan Sen1 3, and Markus J. Buehler1 ∗ 1Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Mass. Ave., Room 1-235A&B, Cambridge, MA, 02139, USA 2Department of Chemical Engineering, Hampton University, Hampton VA, USA 3Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Mass. Ave., Cambridge, MA, 02139, USA

Metal-based nanocomposites provide great potential for applications in high hardness and toughness material design. Potential applications include coatings for friction and wearresistant cutting tools, shock impact-dissipating structures, and other tribological applications where strong functional materials are the key to initiate further technological development. [1–4] Recent advances in the development of nanocomposite materials have suggested that a new paradigm of composite design might be to systematically engineer the nanostructural arrangement of components by designing their properties, interfaces, and geometry to tailor desired macroscopic functional properties. These efforts extend earlier studies of creating nanomaterials out of metallic constituents (e.g. nanowires, thin films) toward bulk materials. [5] However, the optimal choice of nanostructural arrangement of material constituents to maximize performance remains unknown, preventing us from systematically carrying out a bottom-up design approach. A variety of biological structural materials such as bone and nacre are known to feature a common ‘‘brick-and-mortar’’ structural motifs at the nanoscale, composed of material constituents with disparate properties (Fig. 1). [6–10] These universal nanostructures are seen to combine inferior building materials, soft protein, and brittle calcite or hydroxyapatite crystals to obtain structures with high strength and high toughness at biological scales. [11–13] Their improved properties have been attributed to their hierarchical structure, as well as their fundamental structural organization of constituting elements at the nanoscale. [14] The biological role of these materials is strongly related to load carrying and armor protection in nature. Based on their intriguing properties, these materials raise an important question whether their design strategies could provide directions for conventional structural engineering material design. However, for materials development, the use of proteins and platelets is not a viable option, because these materials are rather difficult to synthesize and engineer. Here, we propose an alternative approach, based on using metal–metal nanocomposites that utilize the material concepts identified from biological analogs as guiding principles in the design process. However, despite earlier studies, [15,16] the transferability of designs found in biological structures toward conventional metal and ceramic based composites remains an unresolved question, partly because the fundamental mechanisms of how structure and properties are related have not yet been explored. Specifically, the wide parameter space associated with different platelet shapes and orientations has not been described in the literature.

We report a novel multi-paradigm multi-scale approach based on a combination of the first principles ReaxFF force field with an empirical Tersoff potential. Our hybrid multi-scale simulation model is computationally efficient and capable of treating thousands of atoms with QM accuracy, extending our ability to simulate the dynamical behavior of a wider range of chemically complex materials such as silicon, silica and metal-organic compounds. It is implemented in the Python based Computational Materials Design Facility (CMDF). We exemplify our method in a study focused on a systematic comparison of the fracture dynamics in silicon under mode II shear versus mode I tensile loading. We find that the mode II crack tends to branch at an angle of approximately 45 degrees once the crack speed approaches 38% of the Rayleigh-wave speed. In contrast, the mode I crack continuously propagates in the direction of the initial crack, and only makes a slight change of direction towards 10 degrees once fracture instabilities occur. Our results reveal fundamental differences of fracture dynamics under mode I versus mode II loading.

Nanostructured composites inspired by structural biomaterials such as bone and nacre form intriguing design templates for biomimetic materials. Here we use large scale molecular dynamics to study the shock response of nanocomposites with similar nanoscopic structural features as bone, to determine whether bioinspired nanostructures provide an improved shock mitigating performance. The utilization of these nanostructures is motivated by the toughness of bone under tensile load, which is far greater than its constituent phases and greater than most synthetic materials. To facilitate the computational experiments, we develop a modified version of an Embedded Atom Method (EAM) alloy multi-body interatomic potential to model the mechanical and physical properties of dissimilar phases of the biomimetic bone nanostructure. We find that the geometric arrangement and the specific length scales of design elements at nanoscale does not have a significant effect on shock dissipation, in contrast to the case of tensile loading where the nanostructural length scales strongly influence the mechanical properties. We find that interfacial sliding between the composite’s constituents is a major source of plasticity under shock loading. Based on this finding, we conclude that controlling the interfacial strength can be used to design a material with larger shock absorption. These observations provide valuable insight towards improving the design of nanostructures in shock-absorbing applications, and suggest that by tuning the interfacial properties in the nanocomposite may provide a path to design materials with enhanced shock absorbing capability.

Mineralized biological materials such as bone, sea sponges or diatoms provide load-bearing and armor functions and universally feature structural hierarchies from nano to macro. Here we report a systematic investigation of the effect of hierarchical structures on toughness and defect-tolerance based on a single and mechanically inferior brittle base material, silica, using a bottom-up approach rooted in atomistic modeling. Our analysis reveals drastic changes in the material crack-propagation resistance (R-curve) solely due to the introduction of hierarchical structures that also result in a vastly increased toughness and defect-tolerance, enabling stable crack propagation over an extensive range of crack sizes. Over a range of up to four hierarchy levels, we find an exponential increase in the defect-tolerance approaching hundred micrometers without introducing additional mechanisms or materials. This presents a significant departure from the defect-tolerance of the base material, silica, which is brittle and highly sensitive even to extremely small nanometer-scale defects.

At low temperatures silicon is a brittle material that shatters catastrophically, whereas at elevated temperatures, the behavior of silicon changes drastically over a narrow temperature range and suddenly becomes ductile. This brittle-to-ductile transition has been observed in experimental studies, yet its fundamental mechanisms remain unknown. Here we report an atomistic-level study of a fundamental event in this transition, the change from brittle cleavage fracture to dislocation emission at crack tips, using the first principles based reactive force field. By solely raising the temperature, we observe an abrupt change from brittle cracking to dislocation emission from a crack within a ≈10 K temperature interval.

Graphene is a truly two-dimensional atomic crystal with exceptional electronic and mechanical properties. Whereas conventional bulk and thin-film materials have been studied extensively, the key mechanical properties of graphene, such as tearing and cracking, remain unknown, partly due to its two-dimensional nature and ultimate single-atom-layer thickness, which result in the breakdown of conventional material models. By combining first-principles ReaxFF molecular dynamics and experimental studies, a bottom-up investigation of the tearing of graphene sheets from adhesive substrates is reported, including the discovery of the formation of tapered graphene nanoribbons. Through a careful analysis of the underlying molecular rupture mechanisms, it is shown that the resulting nanoribbon geometry is controlled by both the graphene–substrate adhesion energy and by the number of torn graphene layers. By considering graphene as a model material for a broader class of two-dimensional atomic crystals, these results provide fundamental insights into the tearing and cracking mechanisms of highly confined nanomaterials., United States. Defense Advanced Research Projects Agency (Grant HR0011-08-1-0067), United States. Army Research Office. (Grant W911NF-06-1-0291)

Silicon is an important material not only for semiconductor applications, but also for the development of novel bioinspired and biomimicking materials and structures or drug delivery systems in the context of nanomedicine. For these applications, a thorough understanding of the fracture behavior of the material is critical. In this paper we address this issue by investigating a fundamental issue of the mechanical properties of silicon, its behavior under extreme mechanical loading. Earlier experimental work has shown that at low temperatures, silicon is a brittle material that fractures catastrophically like glass once the applied load exceeds a threshold value. At elevated temperatures, however, the behavior of silicon is ductile. This brittle-to-ductile transition (BDT) has been observed in many experimental studies of single crystals of silicon. However, the mechanisms that lead to this change in behavior remain questionable, and the atomic-scale phenomena are unknown. Here we report for the first time the direct atomistic simulation of the nucleation of dislocations from a crack tip in silicon only due to an increase of the temperature, using large-scale atomistic simulation with the first principles based ReaxFF force field. By raising the temperature in a computational experiment with otherwise identical boundary conditions, we show that the material response changes from brittle cracking to emission of a dislocation at the crack tip, representing evidence for a potential mechanisms of dislocation mediated ductility in silicon.

Multi-scale properties of biological protein materials have been the focal point of extensive investigations over the past decades, leading to formation of a research field that connects biology and materials science, referred to as materiomics. In this chapter we review atomistic based modeling approaches applied to study the scale-dependent mechanical behavior of biological protein materials, focused on mechanical deformation and failure properties. Specific examples are provided to illustrate the application of numerical methods that link atomistic to mesoscopic and larger continuum scales. The discussion includes the formulation of atomistic simulation methods, as well as examples that demonstrate their application in case studies focused on size effects of the fracture behavior of protein materials. The link of atomistic scale features of molecular structures to structural scales at length-scales of micrometers will be discussed in the analysis of the mechanics of a simple model of the nuclear lamin network, revealing how protein networks with structural flaws cope with mechanical load