Intercalated water layers promote thermal dissipation at bio–nano interfaces

The increasing interest in developing nanodevices for biophysical and biomedical applications results in concerns about thermal management at interfaces between tissues and electronic devices. However, there is neither sufficient knowledge nor suitable tools for the characterization of thermal properties at interfaces between materials of contrasting mechanics, which are essential for design with reliability. Here we use computational simulations to quantify thermal transfer across the cell membrane–graphene interface. We find that the intercalated water displays a layered order below a critical value of ∼1 nm nanoconfinement, mediating the interfacial thermal coupling, and efficiently enhancing the thermal dissipation. We thereafter develop an analytical model to evaluate the critical value for power generation in graphene before significant heat is accumulated to disturb living tissues. These findings may provide a basis for the rational design of wearable and implantable nanodevices in biosensing and thermotherapic treatments where thermal dissipation and transport processes are crucial.MIT-China seed fundNational Natural Science Foundation of China (Grant No. 11472150)National Natural Science Foundation of China (Grant No. 2015CB351900)United States. Office of Naval Research (Grant No. N00014-16-1-233)United States. Office of Naval Research. Presidential Early Career Award for Scientists and Engineers (Grant No. N00014-10-1-0562)United States. Air Force. Office of Scientific Research. FATE MURI (Grant No. FA9550-15-1-0514)United States. Defense Advanced Research Projects AgencyMIT Energy InitiativeNational Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (award number DMR-0819762

Spin excitations in optimally P-doped BaFe2(As0.7P0.3)2superconductor

We use inelastic neutron scattering to study temperature and energy dependence of spin excitations in optimally P-doped BaFe2(As0.7P0.3)2 superconductor (Tc = 30 K) throughout the Brillouin zone. In the undoped state, spin waves and paramagnetic spin excitations of BaFe2As2 stem from antiferromagnetic (AF) ordering wave vector QAF= (1/-1,0) and peaks near zone boundary at (1/-1,1/-1) around 180 meV. Replacing 30% As by smaller P to induce superconductivity, low-energy spin excitations of BaFe2(As0.7P0.3)2form a resonance in the superconducting state and high-energy spin excitations now peaks around 220 meV near (1/-1,1/-1). These results are consistent with calculations from a combined density functional theory and dynamical mean field theory, and suggest that the decreased average pnictogen height in BaFe2(As0.7P0.3)2 reduces the strength of electron correlations and increases the effective bandwidth of magnetic excitations.Comment: 7 pages, 5 figures, with supplementar

A Data-Driven Approach to Morphogenesis under Structural Instability

Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design

Dynamics of wetlands and their effects on carbon emissions in China coastal region - Case study in Bohai Economic Rim

Wetlands are one of the largest carbon sinks in the world due to their large carbon storage, potential for carbon sequestration in peat formation, sediment deposition and plant biomass. However, rapid economic development is causing changes to wetland carbon storage. China has participated in the implementation of the Kyoto Protocol and is decreasing its carbon emissions. Analyzing the carbon changes that are caused by wetland dynamics may provide some insights regarding decreasing carbon emissions. Therefore, wetland data from 1985, 1995 and 2005 were extracted from remote sensing images. Using spatial analysis and statistics, we determined that the water body area continued to increase, whereas the swamp, floodplain and shallow areas tended to decrease during the period from 1985 to 2005. Those changes caused wetland carbon stock to decrease. The conversion of other land use categories to wetland was the primary cause of carbon stock loss. Therefore, it is more beneficial for China to decrease per capita carbon emissions by decreasing carbon emissions from the conversion of other land use categories to wetlands. (C) 2013 Elsevier Ltd. All rights reserved.Wetlands are one of the largest carbon sinks in the world due to their large carbon storage, potential for carbon sequestration in peat formation, sediment deposition and plant biomass. However, rapid economic development is causing changes to wetland carbon storage. China has participated in the implementation of the Kyoto Protocol and is decreasing its carbon emissions. Analyzing the carbon changes that are caused by wetland dynamics may provide some insights regarding decreasing carbon emissions. Therefore, wetland data from 1985, 1995 and 2005 were extracted from remote sensing images. Using spatial analysis and statistics, we determined that the water body area continued to increase, whereas the swamp, floodplain and shallow areas tended to decrease during the period from 1985 to 2005. Those changes caused wetland carbon stock to decrease. The conversion of other land use categories to wetland was the primary cause of carbon stock loss. Therefore, it is more beneficial for China to decrease per capita carbon emissions by decreasing carbon emissions from the conversion of other land use categories to wetlands. (C) 2013 Elsevier Ltd. All rights reserved

Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making

Physics-Transfer Learning for Material Strength Screening

The strength of materials, like many problems in the natural sciences, spans multiple length and time scales, and the solution has to balance accuracy and performance. Peierls stress is one of the central concepts in crystal plasticity that measures the strength through the resistance of a dislocation to plastic flow. The determination of Peierls stress involves a multiscale nature depending on both elastic lattice responses and the energy landscape of crystal slips. Material screening by strength via the Peierls stress from first-principles calculations is computationally intractable for the nonlocal characteristics of dislocations, and not included in the state-of-the-art computational material databases. In this work, we propose a physics-transfer framework to learn the physics of crystal plasticity from empirical atomistic simulations and then predict the Peierls stress from chemically accurate density functional theory-based calculations of material parameters. Notably, the strengths of single-crystalline metals can be predicted from a few single-point calculations for the deformed lattice and on the {\gamma} surface, allowing efficient, high-throughput screening for material discovery. Uncertainty quantification is carried out to assess the accuracy of models and sources of errors, showing reduced physical and system uncertainties in the predictions by elevating the fidelity of training models. This physics-transfer framework can be generalized to other problems facing the accuracy-performance dilemma, by harnessing the hierarchy of physics in the multiscale models of materials science

An Expectation Maximization Algorithm to Model Failure Times by Continuous-Time Markov Chains

In many applications, the failure rate function may present a bathtub shape curve. In this paper, an expectation maximization algorithm is proposed to construct a suitable continuous-time Markov chain which models the failure time data by the first time reaching the absorbing state. Assume that a system is described by methods of supplementary variables, the device of stage, and so on. Given a data set, the maximum likelihood estimators of the initial distribution and the infinitesimal transition rates of the Markov chain can be obtained by our novel algorithm. Suppose that there are m transient states in the system and that there are n failure time data. The devised algorithm only needs to compute the exponential of m×m upper triangular matrices for O(nm2) times in each iteration. Finally, the algorithm is applied to two real data sets, which indicates the practicality and efficiency of our algorithm