Publications

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Understanding how science and technology advance has long been of interest to diverse scholarly communities. Thus far, however, such understanding has not been easy to map to, and thus to improve, the operational practice of research and development. Indeed, one might argue that the operational practice of research and development, particularly its exploratory research half, has become less effective in recent decades. In this paper, we describe a rethinking of how science and technology advance, one that is consistent with many (though not all) of the perspectives of the scholarly communities just mentioned, and one that helps bridge the divide between theory and practice. The result is an architecture we call “Bell's Dodecants,” to reflect its six mechanisms and two flavors, and their balanced nurturing at Bell Labs, the iconic 20th century industrial research and development laboratory.

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Metal-organic frameworks (MOFs) have shown promise for adsorptive separations of metal ions. Herein, MOFs based on highly stable Zr(iv) building units were systematically functionalized with targeted metal binding groups. Through competitive adsorption studies, it was shown that the selectivity for different metal ions was directly tunable through functional group chemistry.

K-means clustering analysis is applied to frequency-domain thermoreflectance (FDTR) hyperspectral image data to rapidly screen the spatial distribution of thermophysical properties at material interfaces. Performing FDTR while raster scanning a sample consisting of 8.6 μ m of doped-silicon (Si) bonded to a doped-Si substrate identifies spatial variation in the subsurface bond quality. Routine thermal analysis at select pixels quantifies this variation in bond quality and allows assignment of bonded, partially bonded, and unbonded regions. Performing this same routine thermal analysis across the entire map, however, becomes too computationally demanding for rapid screening of bond quality. To address this, K-means clustering was used to reduce the dimensionality of the dataset from more than 20 000 pixel spectra to just K = 3 component spectra. The three component spectra were then used to express every pixel in the image through a least-squares minimized linear combination providing continuous interpolation between the components across spatially varying features, e.g., bonded to unbonded transition regions. Fitting the component spectra to the thermal model, thermal properties for each K cluster are extracted and then distributed according to the weighting established by the regressed linear combination. Thermophysical property maps are then constructed and capture significant variation in bond quality over 25 μ m length scales. The use of K-means clustering to achieve these thermal property maps results in a 74-fold speed improvement over explicit fitting of every pixel.

The frequency response function (FRF) is an essential means by which dynamic systems are qualified. In recent years, local modeling approaches have been extensively researched and shown to significantly outperform traditional FRF estimators. However, the standard local modeling approach assumes a perfectly-known system input, which results in biased FRF estimates in the presence of input noise. This paper derives a simple adjustment that can be used to improve FRF estimation for systems subjected to random excitation with noisy input data. This improvement can be implemented with little modification to standard local modeling algorithms and with little additional computational burden. The adjustment is coupled with a model selection procedure to avoid underfitting and overfitting. The methods presented in this paper are validated on a simulation, and they are shown to reduce bias due to input noise.

Risks associated with carbonation are a key limitation to greater replacement levels of ordinary portland cement (OPC) by supplementary cementitious materials (SCMs). The addition of pozzolanic SCMs in OPC alters the hydrate assemblage by forming phases like calcium-(alumina)-silicate-hydrate (C-(A)-S-H). The objective of the present study was to elucidate how such changes in hydrate assemblage influence the chemical mechanisms of carbonation in a realistic OPC system. Here, we show that synthetic zeolite Y (faujasite) is a highly reactive pozzolan in OPC that reduces the calcium content of hydration products via prompt consumption of calcium hydroxide from the evolving phase assemblage prior to CO2 exposure. Suppression of portlandite at moderate to high zeolite Y content led to a more damaging mechanism of carbonation by disrupting the formation of a passivating carbonate layer. Without this layer, carbonation depth and CO2 uptake are increased. Binders containing 12–18% zeolite Y by volume consumed all the calcium hydroxide from OPC during hydration and reduced the Ca/(Si+Al) ratio of the amorphous products to near 0.67. In these cases, higher carbonation depths were observed after exposure to ambient air with decalcification of C-(A)-S-H as the main source of CO2 buffering. Binders with either 0% or 4% zeolite Y contained calcium hydroxide in the hydrated microstructure, had higher Ca/(Si+Al) ratios, and formed a calcite-rich passivation layer that halted deep carbonation. Although the carbonated layer in the samples with 12% and 18% zeolite Y contained 70% and 76% less calcite than the OPC respectively, their higher carbonation depths resulted in total CO2 uptakes that were 12x greater than the OPC sample. Passivation layer formation in samples with calcium hydroxide explains this finding and was further supported by thermodynamic modeling. High Si/Al zeolite additives to OPC should be balanced with the calcium content for optimal carbonation resistance.

Z-pinch platforms constitute a promising pathway to fusion energy research. Here, we present a one-dimensional numerical study of the staged Z-pinch (SZP) concept using the FLASH and MACH2 codes. We discuss the verification of the codes using two analytical benchmarks that include Z-pinch-relevant physics, building confidence on the codes’ ability to model such experiments. Then, FLASH is used to simulate two different SZP configurations: a xenon gas-puff liner (SZP1*) and a silver solid liner (SZP2). The SZP2 results are compared against previously published MACH2 results, and a new code-to-code comparison on SZP1* is presented. Using an ideal equation of state and analytical transport coefficients, FLASH yields a fuel convergence ratio (CR) of approximately 39 and a mass-averaged fuel ion temperature slightly below 1 keV for the SZP2 scheme, significantly lower than the full-physics MACH2 prediction. For the new SZP1* configuration, full-physics FLASH simulations furnish large and inherently unstable CRs (> 300), but achieve fuel ion temperatures of many keV. While MACH2 also predicts high temperatures, the fuel stagnates at a smaller CR. The integrated code-to-code comparison reveals how magnetic insulation, heat conduction, and radiation transport affect platform performance and the feasibility of the SZP concept.

Since the earliest investigations of olefin metathesis catalysis, light has been the choice for controlling the catalyst activity on demand. From the perspective of energy efficiency, temporal and spatial control, and selectivity, photochemistry is not only an attractive alternative to traditional thermal manufacturing techniques but also arguably a superior manifold for advanced applications like additive manufacturing (AM). In the last three decades, pioneering work in the field of ring-opening metathesis polymerization (ROMP) has broadened the scope of material properties achievable through AM, particularly using light as both an activating and deactivating stimulus. In this Perspective, we explore trends in photocontrolled ROMP systems with an emphasis on approaches to photoinduced activation and deactivation of metathesis catalysts. Recent work has yielded a myriad of commercial and synthetically accessible photosensitive catalyst systems, although comparatively little attention has been paid to achieving precise control over polymer morphology using light. Metal-free, photophysical, and living ROMP systems have also been relatively underexplored. To take fuller advantage of both the thermomechanical properties of ROMP polymers and the operational simplicity of photocontrol, clear directions for the field are to improve the reversibility of activation and deactivation strategies as well as to further develop photocontrolled approaches to tuning cross-link density and polymer tacticity.

We present optical Thomson scattering measurements of electron density and temperature in high Mach number laser-driven blast waves in homogeneous gases. Taylor–Sedov blast waves are launched in nitrogen (N₂) or helium (He) at pressures between 0.4 mTorr and 10 Torr by ablating a solid plastic target with a high energy laser pulse (10 J, 10¹² W/cm²). Experiments are performed at high repetition rate (1 Hz), which allows one-dimensional and two-dimensional Thomson scattering measurements over an area of several cm² by automatically translating the scattering volume between shots. Electron temperature and density in the blast wave fronts were seen to increase with increasing background gas pressure. Measured electron density and temperature gradients were used to calculate $\partial$B/$\partial$t ∝ ∇T_e $\times$ ∇n_e⁠. The experimentally measured $\partial$B/$\partial$t showed agreement with the magnetic field probe (B-dot) measurements, revealing that magnetic fields are generated in the observed blast waves via the Biermann battery effect. The results are compared to numerical three-dimensional collisional magnetohydrodynamic simulations performed with FLASH, and are discussed in the context of spontaneous magnetic field generation via the Biermann battery effect.

Liquid crystal elastomers (LCEs) exhibit unique mechanical properties of soft elasticity and enhanced energy dissipation with rate dependency. They are potentially transformative materials for applications in mechanical impact mitigation and vibration isolation. However, previous studies have primarily focused on the mechanics of LCEs under equilibrium and quasistatic loading conditions. Critical knowledge gaps exist in understanding their rate-dependent behaviors, which are a complex mixture of traditional network viscoelasticity and the soft elastic behaviors with changes in the mesogen orientation and order parameter. Together, these inelastic mechanisms lead to unusual rate-dependent energy absorption responses of LCEs. In this work, we developed a viscoelastic constitutive theory for monodomain nematic LCEs to investigate how multiple underlying sources of inelasticity manifest in the rate-dependent and dissipative behaviors of monodomain LCEs. The theoretical modeling framework combines the neo-classical network theory with evolution rules for the mesogen orientation and order parameter with conventional viscoelasticity. The model is calibrated with uniaxial tension and compression data spanning six decades of strain rates. The established 3D constitutive model enables general loading predictions taking the initial mesogen orientation and order parameter as inputs. Additionally, parametric studies were performed to further understand the rate dependence of monodomain LCEs in relation to their energy absorption characteristics. Based on the parametric studies, particularly loading scenarios are identified as conditions where LCEs outperform conventional elastomers regarding energy absorption.

Filamentous fungi are known to secrete biochemicals that drive the synthesis of nanoparticles (NPs) that vary in composition, size, and shape; a process deemed mycosynthesis. Following the introduction of precursor salts directly to the fungal mycelia or their exudates, mycosynthesis proceeds at ambient temperature and pressure, and near neutral pH, presenting significant energy and cost savings over traditional chemical or physical approaches. The mycosynthesis of zinc oxide (ZnO) NPs by various fungi exhibited a species dependent morphological preference for the resulting NPs, suggesting that key differences in the biochemical makeup of their individual exudates may regulate the controlled nucleation and growth of these different morphologies. Metabolomics and proteomics of the various fungal exudates suggest that metal chelators, such as hexamethylenetetramine, present in high concentrations in exudates of Aspergillus versicolor are critical for the production dense, well-formed, spheroid nanoparticles. Further, the results also corroborate that the proteinaceous material in the production of ZnO NPs serves as a surface modifier, or protein corona, preventing excessive coagulation of the NPs. Collectively, these findings suggest that NP morphology is regulated by the small molecule metabolites, and not proteins, present in fungal exudates, establishing a deeper understanding of the factors and mechanism underlying mycosynthesis of NPs.