Extreme Gradient Boosting to Predict Atomic Layer Deposition for Platinum Nano-Film Coating.

Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model development, platinum is coated on α-Al2O3 using a rotary-type ALD equipment. The process is controlled by four parameters: process temperature, stop valve time, precursor pulse time, and reactant pulse time. A total of 625 samples according to different process conditions are obtained. The ALD coating index is used as the Al/Pt component ratio through ICP-AES analysis during postprocessing. The four process parameters serve as the input data and produces the Al/Pt component ratio as the output data. The postprocessed data set is randomly divided into 500 training samples and 125 test samples. XGBoost demonstrates 99.9% accuracy and a coefficient of determination of 0.99. The inference time is lower than that of random forest regression, in addition to a higher prediction safety than that of the light gradient boosting machine.

Development of user customized smart keyboard using Smart Product Design-Finite Element Analysis Process in the Internet of Things.

In a hyper-connected society, IoT environment, markets are rapidly changing as smartphones penetrate global market. As smartphones are applied to various digital media, development of a novel smart product is required. In this paper, a Smart Product Design-Finite Element Analysis Process (SPD-FEAP) is developed to adopt fast-changing tends and user requirements that can be visually verified. The user requirements are derived and quantitatively evaluated from Smart Quality Function Deployment (SQFD) using WebData. Then the usage scenarios are created according to the priority of the functions derived from SQFD. 3D shape analysis by Finite Element Analysis (FEA) was conducted and printed out through Rapid Prototyping (RP) technology to identify any possible errors. Thus, a User Customized Smart Keyboard has been developed using SPD-FEAP.

Normal mode analysis of Zika virus.

In recent years, Zika virus (ZIKV) caused a new pandemic due to its rapid spread and close relationship with microcephaly. As a result, ZIKV has become an obvious global health concern. Information about the fundamental viral features or the biological process of infection remains limited, despite considerable efforts. Meanwhile, the icosahedral shell structure of the mature ZIKV was recently revealed by cryo-electron microscopy. This structural information enabled us to simulate ZIKV. In this study, we analyzed the dynamic properties of ZIKV through simulation from the mechanical viewpoint. We performed normal mode analysis (NMA) for a dimeric structure of ZIKV consisting of the envelope proteins and the membrane proteins as a unit structure. By analyzing low-frequency normal modes, we captured intrinsic vibrational motions and defined basic vibrational properties of the unit structure. Moreover, we also simulated the entire shell structure of ZIKV at the reduced computational cost, similar to the case of the unit structure, by utilizing its icosahedral symmetry. From the NMA results, we can not only comprehend the putative dynamic fluctuations of ZIKV but also verify previous inference such that highly mobile glycosylation sites would play an important role in ZIKV. Consequently, this theoretical study is expected to give us an insight on the underlying biological functions and infection mechanism of ZIKV.

Normal mode-guided transition pathway generation in proteins.

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Dynamic characteristics of a flagellar motor protein analyzed using an elastic network model.

At the base of a flagellar motor, its rotational direction and speed are regulated by the interaction between rotor and stator proteins. A switching event occurs when the cytoplasmic rotor protein, called C-ring, changes its conformation in response to binding of the CheY signal protein. The C-ring structure consists of FliG, FliM, and FliN proteins and its conformational changes in FliM and FliG including HelixMC play an important role in switching the motor direction. Therefore, clarifying their dynamic properties as well as conformational changes is a key to understanding the switching mechanism of the motor protein. In this study, to elucidate dynamic characteristics of the C-ring structure, both harmonic (intrinsic vibration) and anharmonic (transition pathway) analyses are conducted by using the symmetry-constrained elastic network model. As a result, the first three normal modes successfully capture the essence of transition pathway from wild type to CW-biased state. Their cumulative square overlap value reaches up to 0.842. Remarkably, it is also noted from the transition pathway that the cascade of interactions from the signal protein to FliM to FliG, highlighted by the major mode shapes from the first three normal modes, induces the reorientation (∼100° rotation of FliGC5) of FliG C-terminal that directly interacts with the stator protein. Presumably, the rotational direction of the motor protein is switched by this substantial change in the stator-rotor interaction.

Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model.

Agonist-activated G protein-coupled receptors (GPCRs) interact with GDP-bound G protein heterotrimers (Gαßγ) promoting GDP/GTP exchange, which results in dissociation of Gα from the receptor and Gßγ. The GTPase activity of Gα hydrolyzes GTP to GDP, and the GDP-bound Gα interacts with Gßγ, forming a GDP-bound G protein heterotrimer. The G protein cycle is allosterically modulated by conformational changes of the Gα subunit. Although biochemical and biophysical methods have elucidated the structure and dynamics of Gα, the precise conformational mechanisms underlying the G protein cycle are not fully understood yet. Simulation methods could help to provide additional details to gain further insight into G protein signal transduction mechanisms. In this study, using the available X-ray crystal structures of Gα, we simulated the entire G protein cycle and described not only the steric features of the Gα structure, but also conformational changes at each step. Each reference structure in the G protein cycle was modeled as an elastic network model and subjected to normal mode analysis. Our simulation data suggests that activated receptors trigger conformational changes of the Gα subunit that are thermodynamically favorable for opening of the nucleotide-binding pocket and GDP release. Furthermore, the effects of GTP binding and hydrolysis on mobility changes of the C and N termini and switch regions are elucidated. In summary, our simulation results enabled us to provide detailed descriptions of the structural and dynamic features of the G protein cycle.

Understanding the nanoscale local buckling behavior of vertically aligned MWCNT arrays with van der Waals interactions.

The local buckling behavior of vertically aligned carbon nanotubes (VACNTs) has been investigated and interpreted in the view of a collective nanotube response by taking van der Waals interactions into account. To the best of our knowledge, this is the first report on the case of collective VACNT behavior regarding van der Waals force among nanotubes as a lateral support effect during the buckling process. The local buckling propagation and development of VACNTs were experimentally observed and theoretically analyzed by employing finite element modeling with lateral support from van der Waals interactions among nanotubes. Both experimental and theoretical analyses show that VACNTs buckled in the bottom region with many short waves and almost identical wavelengths, indicating a high mode buckling. Furthermore, the propagation and development mechanism of buckling waves follow the wave damping effect.

Determination of material constants of vertically aligned carbon nanotube structures in compressions.

Different chemical vapour deposition (CVD) fabrication conditions lead to a wide range of variation in the microstructure and morphologies of carbon nanotubes (CNTs), which actually determine the compressive mechanical properties of CNTs. However, the underlying relationship between the structure/morphology and mechanical properties of CNTs is not fully understood. In this study, we characterized and compared the structural and morphological properties of three kinds of vertically aligned carbon nanotube (VACNT) arrays from different CVD fabrication methods and performed monotonic compressive tests for each VACNT array. The compressive stress-strain responses and plastic deformation were first compared and analyzed with nanotube buckling behaviours. To quantify the compressive properties of the VACNT arrays, a strain density energy function was used to determine their intrinsic material constants. Then, the structural and morphological effects on the quantified material constants of the VACNTs were statistically investigated and analogized to cellular materials with an open-cell model. The statistical analysis shows that density, defect degree, and the moment of inertia of the CNTs are key factors in the improvement of the compressive mechanical properties of VACNT arrays. This approach could allow a model-driven CNT synthesis for engineering their mechanical behaviours.

Reduced Water Vapor Transmission Rate of Graphene Gas Barrier Films for Flexible Organic Field-Effect Transistors.

Preventing reactive gas species such as oxygen or water is important to ensure the stability and durability of organic electronics. Although inorganic materials have been predominantly employed as the protective layers, their poor mechanical property has hindered the practical application to flexible electronics. The densely packed hexagonal lattice of carbon atoms in graphene does not allow the transmission of small gas molecules. In addition, its outstanding mechanical flexibility and optical transmittance are expected to be useful to overcome the current mechanical limit of the inorganic materials. In this paper, we reported the measurement of the water vapor transmission rate (WVTR) through the 6-layer 10 × 10 cm(2) large-area graphene films synthesized by chemical vapor deposition (CVD). The WVTR was measured to be as low as 10(-4) g/m(2)·day initially, and stabilized at ∼0.48 g/m(2)·day, which corresponds to 7 times reduction in WVTR compared to bare polymer substrates. We also showed that the graphene-passivated organic field-effect transistors (OFETs) exhibited excellent environmental stability as well as a prolonged lifetime even after 500 bending cycles with strain of 2.3%. We expect that our results would be a good reference showing the graphene's potential as gas barriers for organic electronics.

An Ag-grid/graphene hybrid structure for large-scale, transparent, flexible heaters.

Recently, carbon materials such as carbon nanotubes and graphene have been proposed as alternatives to indium tin oxide (ITO) for fabricating transparent conducting materials. However, obtaining low sheet resistance and high transmittance of these carbon materials has been challenging due to the intrinsic properties of the materials. In this paper, we introduce highly transparent and flexible conductive films based on a hybrid structure of graphene and an Ag-grid. Electrohydrodynamic (EHD) jet printing was used to produce a micro-scale grid consisting of Ag lines less than 10 µm wide. We were able to directly write the Ag-grid on a large-area graphene/flexible substrate due to the high conductivity of graphene. The hybrid electrode could be fabricated using hot pressing transfer and EHD jet printing in a non-vacuum, maskless, and low-temperature environment. The hybrid electrode offers an effective and simple route for achieving a sheet resistance as low as ∼4 Ω per square with ∼78% optical transmittance. Finally, we demonstrate that transparent flexible heaters based on the hybrid conductive films could be used in a vehicle or a smart window system.

Laminated ultrathin chemical vapor deposition graphene films based stretchable and transparent high-rate supercapacitor.

Due to their exceptional flexibility and transparency, CVD graphene films have been regarded as an ideal replacement of indium tin oxide for transparent electrodes, especially in applications where electronic devices may be subjected to large tensile strain. However, the search for a desirable combination of stretchability and electrochemical performance of such devices remains a huge challenge. Here, we demonstrate the implementation of a laminated ultrathin CVD graphene film as a stretchable and transparent electrode for supercapacitors. Transferred and buckled on PDMS substrates by a prestraininig-then-buckling strategy, the four-layer graphene film maintained its outstanding quality, as evidenced by Raman spectra. Optical transmittance of up to 72.9% at a wavelength of 550 nm and stretchability of 40% were achieved. As the tensile strain increased up to 40%, the specific capacitance showed no degradation and even increased slightly. Furthermore, the supercapacitor demonstrated excellent frequency capability with small time constants under stretching.

A highly conducting graphene film with dual-side molecular n-doping.

Doping is an efficient way to engineer the conductivity and the work function of graphene, which is, however, limited to wet-chemical doping or metal deposition particularly for n-doping, Here, we report a simple method of modulating the electrical conductivity of graphene by dual-side molecular n-doping with diethylenetriamine (DETA) on the top and amine-functionalized self-assembled monolayers (SAMs) at the bottom. The resulting charge carrier density of graphene is as high as -1.7 × 10(13) cm(-2), and the sheet resistance is as low as ∼86 ± 39 Ω sq(-1), which is believed to be the lowest sheet resistance of monolayer graphene reported so far. This facile dual-side n-doping strategy would be very useful to optimize the performance of various graphene-based electronic devices.

Vibrational characteristics of graphene sheets elucidated using an elastic network model.

Recent studies of graphene have demonstrated its great potential for highly sensitive resonators. In order to capture the intrinsic vibrational characteristics of graphene, we propose an atomistic modeling method called the elastic network model (ENM), in which a graphene sheet is modeled as a mass-spring network of adjacent atoms connected by various linear springs with specific bond ratios. Normal mode analysis (NMA) reveals the various vibrational features of bi-layer graphene sheets (BLGSs) clamped at two edges. We also propose a coarse-graining (CG) method to extend our graphene study into the meso- and macroscales, at which experimental measurements and synthesis of graphene become practical. The simulation results show good agreement with experimental observations. Therefore, the proposed ENM approach will not only shed light on the theoretical study of graphene mechanics, but also play an important role in the design of highly-sensitive graphene-based resonators.

Opening and closing of a toroidal group II chaperonin revealed by a symmetry constrained elastic network model.

Recently, the atomic structures of both the closed and open forms of Group 2 chaperonin protein Mm-cpn were revealed through crystallography and cryo-electron microscopy. This toroidal-like chaperonin is composed of two eightfold rings that face back-to-back. To gain a computational advantage, we used a symmetry constrained elastic network model (SCENM), which requires only a repeated subunit structure and its symmetric connectivity to neighboring subunits to simulate the entire system. In the case of chaperonin, only six subunits (i.e., three from each ring) were used out of the eight subunits comprising each ring. A smooth and symmetric pathway between the open and closed conformations was generated by elastic network interpolation (ENI). To support this result, we also performed a symmetry-constrained normal mode analysis (NMA), which revealed the intrinsic vibration features of the given structures. The NMA and ENI results for the representative single subunit were duplicated according to the symmetry pattern to reconstruct the entire assembly. To test the feasibility of the symmetry model, its results were also compared with those obtained from the full model. This study allowed the folding mechanism of chaperonin Mm-cpn to be elucidated by SCENM in a timely manner.

Efficient prediction of protein conformational pathways based on the hybrid elastic network model.

Various computational models have gained immense attention by analyzing the dynamic characteristics of proteins. Several models have achieved recognition by fulfilling either theoretical or experimental predictions. Nonetheless, each method possesses limitations, mostly in computational outlay and physical reality. These limitations remind us that a new model or paradigm should advance theoretical principles to elucidate more precisely the biological functions of a protein and should increase computational efficiency. With these critical caveats, we have developed a new computational tool that satisfies both physical reality and computational efficiency. In the proposed hybrid elastic network model (HENM), a protein structure is represented as a mixture of rigid clusters and point masses that are connected with linear springs. Harmonic analyses based on the HENM have been performed to generate normal modes and conformational pathways. The results of the hybrid normal mode analyses give new physical insight to the 70S ribosome. The feasibility of the conformational pathways of hybrid elastic network interpolation (HENI) was quantitatively evaluated by comparing three different overlap values proposed in this paper. A remarkable observation is that the obtained mode shapes and conformational pathways are consistent with each other. Our timing results show that HENM has some advantage in computational efficiency over a coarse-grained model, especially for large proteins, even though it takes longer to construct the HENM. Consequently, the proposed HENM will be one of the best alternatives to the conventional coarse-grained ENMs and all-atom based methods (such as molecular dynamics) without loss of physical reality.

A mass weighted chemical elastic network model elucidates closed form domain motions in proteins.

An elastic network model (ENM), usually Cα coarse-grained one, has been widely used to study protein dynamics as an alternative to classical molecular dynamics simulation. This simple approach dramatically saves the computational cost, but sometimes fails to describe a feasible conformational change due to unrealistically excessive spring connections. To overcome this limitation, we propose a mass-weighted chemical elastic network model (MWCENM) in which the total mass of each residue is assumed to be concentrated on the representative alpha carbon atom and various stiffness values are precisely assigned according to the types of chemical interactions. We test MWCENM on several well-known proteins of which both closed and open conformations are available as well as three α-helix rich proteins. Their normal mode analysis reveals that MWCENM not only generates more plausible conformational changes, especially for closed forms of proteins, but also preserves protein secondary structures thus distinguishing MWCENM from traditional ENMs. In addition, MWCENM also reduces computational burden by using a more sparse stiffness matrix.

A transparent and stretchable graphene-based actuator for tactile display.

A tactile display is an important tool to help humans interact with machines by using touch. In this paper, we present a transparent and stretchable graphene-based actuator for advanced tactile displays. The proposed actuator is composed of transparent and compliant graphene electrodes and a dielectric elastomer substrate. Since the electrode is coated onto the appointed region of the substrate layer by layer, only the area of the dielectric elastomer substrate with electrodes bumps up in response to the input voltage, which consequently produces actuation. The actuator is proven to be operable while preserving its electrical and mechanical properties even under 25% stretching. Also, the simple fabrication of the proposed actuator is cost-effective and can easily be extended to multiple arrays. The actuator is expected to be applicable to various applications including tactile displays, vari-focal lenses etc.

Evaluation of multi-layered graphene surface plasmon resonance-based transmission type fiber optic sensor.

Graphene is a zero band-gap semi-metal with remarkable electromagnetic and mechanical characteristics. This study is the first ever attempt to use graphene in the surface plasmon resonance (SPR) sensor as replacement material for gold/silver. Graphene, comprised of a single atomic layer of carbon, is a purely two-dimensional material and it is an ideal candidate for use as a biosensor because of its high surface-to-volume ratio. This sensor is based on the resonance occasion of the surface plasmon wave (SPW) according to the dielectric constants of each metal film and detected material in gas or aqueous phase. Graphene in the SPR sensor is expected to enlarge the range of analyte to bio-aerosols based on the superior electromagnetic properties of graphene. In this study, a SPR-based fiber optic sensor coated with multi-layered graphene is described. The multi-layered graphene film synthesized by chemical vapor deposition (CVD) on Ni substrate was transferred on the sensing region of an optical fiber. The graphene coated SPR sensor is used to analyze the interaction between structured DNA biotin and Streptavidin is analyzed. Transmitted light after passing through the sensing region is measured by a spectrometer and multimeter. As the light source, blue light which of 450 to 460 nm in wavelength was used. We observed the SPR phenomena in the sensor and show the contrary trends between bare fiber and graphene coated fiber. The fabricated graphene based fiber optic sensor shows excellent detection sensitivity of the interaction between structured DNA and Streptavidin.

Efficient transfer of large-area graphene films onto rigid substrates by hot pressing.

Graphene films grown on metal substrates by chemical vapor deposition (CVD) method have to be safely transferred onto desired substrates for further applications. Recently, a roll-to-roll (R2R) method has been developed for large-area transfer, which is particularly efficient for flexible target substrates. However, in the case of rigid substrates such as glass or wafers, the roll-based method is found to induce considerable mechanical damages on graphene films during the transfer process, resulting in the degradation of electrical property. Here we introduce an improved dry transfer technique based on a hot-pressing method that can minimize damage on graphene by neutralizing mechanical stress. Thus, we enhanced the transfer efficiency of the large-area graphene films on a substrate with arbitrary thickness and rigidity, evidenced by scanning electron microscope (SEM) and atomic force microscope (AFM) images, Raman spectra, and various electrical characterizations. We also performed a theoretical multiscale simulation from continuum to atomic level to compare the mechanical stresses caused by the R2R and the hot-pressing methods, which also supports our conclusion. Consequently, we believe that the proposed hot-pressing method will be immediately useful for display and solar cell applications that currently require rigid and large substrates.

Detection of acetone vapor using graphene on polymer optical fiber.

Recently, many studies have been focused on the development of fiber optic sensor systems for various gases and vapors. In the present study, an intrinsic polymer optical fiber (POF) sensor using graphene is described for the purpose of acetone vapor sensing for the first time. Observations on the continuous measurement of acetone vapor in dehydrated air are presented. The principle of operation of sensor transduction relies on the dependence of the reflectance on the optical and geometric properties of the sensitive over layered when the vapor molecules are adsorbed on the graphene film. For the same purpose the CVD synthesized graphene film was transferred on the POF end. The synthesized graphene film thickness was evaluated using atomic force microscopy (AFM), Raman spectroscopy and transmission electron microscopy (TEM). For the preliminary evaluation using volatile organic compounds, we evaluated the sensor performance for acetone. Upon the interaction of the sensor with acetone vapor, the variation in the reflected light was monitored as a function of the acetone concentration. The sensor response shows a significant change in sensitivity as compared with the POF probe without a graphene coating. The present sensor shows a satisfactory response upon exposure to various concentrations of acetone vapor from 44 ppm to 352 ppm. To the best of our knowledge, the use of graphene film along with POF for the sensing of volatile organic compounds has not previously been reported.