Project description:Despite recent advances in our understanding of the unique mechanical behavior of natural structural materials such as nacre and human bone, traditional manufacturing strategies limit our ability to mimic such nature-inspired structures using existing structural materials and manufacturing processes. To this end, we introduce a customizable single-step approach for additively fabricating geometrically-free metallic-based structural composites showing directionally-tailored, location-specific properties. To exemplify this capability, we present a layered metal-ceramic composite not previously reported exhibiting significant directional and site-specific dependence of properties along with crack arrest ability difficult to achieve using traditional manufacturing approaches. Our results indicate that nature-inspired microstructural designs towards directional properties can be realized in structural components using a novel additive manufacturing approach.
Project description:Architected materials can achieve impressive shape-changing capabilities according to how their microarchitecture is engineered. Here we introduce an approach for dramatically advancing such capabilities by utilizing wrapped flexure straps to guide the rolling motions of tightly packed micro-cams that constitute the material's microarchitecture. This approach enables high shape-morphing versatility and extreme ranges of deformation without accruing appreciable increases in strain energy or internal stress. Two-dimensional and three-dimensional macroscale prototypes are demonstrated, and the analytical theory necessary to design the proposed materials is provided and packaged as a software tool. An approach that combines two-photon stereolithography and scanning holographic optical tweezers is demonstrated to enable the fabrication of the proposed materials at their intended microscale.
Project description:Surface roughness of electrodes plays a key role in the dielectric breakdown of thin-film organic devices. The rate of breakdown will increase when there are stochastic sharp spikes on the surface of electrodes. Additionally, surface having spiking morphology makes the determination of dielectric strength very challenging, specifically when the layer is relatively thin. We demonstrate here a new approach to investigate the dielectric strength of organic thin films for organic light-emitting diodes (OLEDs). The thin films were deposited on a substrate using physical vapor deposition (PVD) under high vacuum. The device architectures used were glass substrate/indium tin oxide (ITO)/organic material/aluminum (Al) and glass substrate/Al/organic material/Al. The dielectric strength of the OLED materials was evaluated from the measured breakdown voltage and layer thickness.
Project description:Crystal-inspired approach is found to be highly successful in designing extraordinarily damage-tolerant architected materials. i.e. meta-crystals, necessitating in-depth fundamental studies to reveal the underlying mechanisms responsible for the strengthening in meta-crystals. Such understanding will enable greater confidence to control not only strength, but also spatial local deformation. In this study, the mechanisms underlying shear band activities were investigated and discussed to provide a solid basis for predicting and controlling the local deformation behaviour in meta-crystals. The boundary strengthening in polycrystal-like meta-crystals was found to relate to the interaction between shear bands and polygrain-like boundaries. More importantly, the boundary type and coherency were found to be influential as they govern the transmission of shear bands across meta-grains boundaries. The obtained insights in this study provide crucial knowledge in developing high strength architected materials with great capacity in controlling and programming the mechanical strength and damage path.
Project description:Fabrication of functionalized 3D architected materials is achieved by a facile method using functionalized acrylates synthesized via thiol-Michael addition, which are then polymerized using two-photon lithography. A wide variety of functional groups can be attached, from Boc-protected amines to fluoroalkanes. Modification of surface wetting properties and conjugation with fluorescent tags are demonstrated to highlight the potential applications of this technique.
Project description:Cellular solids are instrumental in creating lightweight, strong, and damage-tolerant engineering materials. By extending feature size down to the nanoscale, we simultaneously exploit the architecture and material size effects to substantially enhance structural integrity of architected meta-materials. We discovered that hollow-tube alumina nanolattices with 3D kagome geometry that contained pre-fabricated flaws always failed at the same load as the pristine specimens when the ratio of notch length (a) to sample width (w) is no greater than 1/3, with no correlation between failure occurring at or away from the notch. Samples with (a/w)?>?0.3, and notch length-to-unit cell size ratios of (a/l)?>?5.2, failed at a lower peak loads because of the higher sample compliance when fewer unit cells span the intact region. Finite element simulations show that the failure is governed by purely tensile loading for (a/w)?<?0.3 for the same (a/l); bending begins to play a significant role in failure as (a/w) increases. This experimental and computational work demonstrates that the discrete-continuum duality of architected structural meta-materials may give rise to their damage tolerance and insensitivity of failure to the presence of flaws even when made entirely of intrinsically brittle materials.
Project description:This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.
Project description:Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional 'one-size-fits-all' approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.
Project description:BackgroundSelecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation.ObjectiveWe demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time.MethodsTo validate the method, Bayesian optimization was employed using participants' binary judgements about the intensity of phosphenes elicited through tACS.ResultsWe demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations.ConclusionAlongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals.
Project description:The quantification of body fat plays an important role in the study of numerous diseases. It is common current practice to use the fat area at a single abdominal computed tomography (CT) slice as a marker of the body fat content in studying various disease processes. This paper sets out to answer three questions related to this issue which have not been addressed in the literature. At what single anatomic slice location do the areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) estimated from the slice correlate maximally with the corresponding fat volume measures? How does one ensure that the slices used for correlation calculation from different subjects are at the same anatomic location? Are there combinations of multiple slices (not necessarily contiguous) whose area sum correlates better with volume than does single slice area with volume?The authors propose a novel strategy for mapping slice locations to a standardized anatomic space so that same anatomic slice locations are identified in different subjects. The authors then study the volume-to-area correlations and determine where they become maximal. To address the third issue, the authors carry out similar correlation studies by utilizing two and three slices for calculating area sum.Based on 50 abdominal CT data sets, the proposed mapping achieves significantly improved consistency of anatomic localization compared to current practice. Maximum correlations are achieved at different anatomic locations for SAT and VAT which are both different from the L4-L5 junction commonly utilized currently for single slice area estimation as a marker.The maximum area-to-volume correlation achieved is quite high, suggesting that it may be reasonable to estimate body fat by measuring the area of fat from a single anatomic slice at the site of maximum correlation and use this as a marker. The site of maximum correlation is not at L4-L5 as commonly assumed, but is more superiorly located at T12-L1 for SAT and at L3-L4 for VAT. Furthermore, the optimal anatomic locations for SAT and VAT estimation are not the same, contrary to common assumption. The proposed standardized space mapping achieves high consistency of anatomic localization by accurately managing nonlinearities in the relationships among landmarks. Multiple slices achieve greater improvement in correlation for VAT than for SAT. The optimal locations in the case of multiple slices are not contiguous.