Resource-sensitive global production networks: reconfigured geographies of timber and acoustic guitar manufacturing

This article examines how resource materiality, scarcity, and evolving international environmental regulation shape global production networks (GPNs). Nature-facing elements, including resource scarcity and environmental regulation, have seldom featured in GPN analysis. So, too, GPN analysis emphasizes spatial relations between network actors over temporal change. We extend GPN theorization through a temporal analysis of industrial change, connecting manufacturing to upstream resource materialities and shifting regulation, and to downstream consumers increasingly concerned with provenance and material stewardship. To illustrate, we document a resource-sensitive GPN-acoustic guitar manufacturing-where scarcity of select raw materials (tonewoods) with material qualities of resonance, strength, and beauty, as well as tighter regulation, has spawned shifting economic geographies of new actors who influence the whole GPN. Such actors include specialist extraction firms, salvagers, traders, verification consultants, and customs agents who innovate in procurement and raw material supply risk management. Traditional large guitar manufacturing firms have struggled with regulation and securing consistent resource supply, although smaller lead manufacturing firms have creatively responded via novel procurement methods and marketing, developing closely bound, iterative relationships with specialist timber harvesters, traders, and with emotionally attached consumers. A cohort of tonewood supply firms and guitar manufacturers-especially in Australia, the Pacific Northwest and Canada, key locations of both resource and design expertise-have together altered material stewardship practices and commodity production. Niche strategies derive exchange value from rarity and resource innovation, embracing raw material variability, inconsistent supply, and the need for alternatives. How firms adapt to resource supply security risks, we argue, is an imperative question for GPN analysis

Combinatorial biomaterials discovery strategy to identify new macromolecular cryoprotectants

Cryoprotective agents (CPAs) are typically solvents or small molecules, but there is a need for innovative CPAs to reduce toxicity and increase cell yield, for the banking and transport of cells. Here we use a photochemical high-throughput discovery platform to identify macromolecular cryoprotectants, as rational design approaches are currently limited by the lack of structure–property relationships. Using liquid handling systems, 120 unique polyampholytes were synthesized using photopolymerization with RAFT agents. Cryopreservation screening identified “hit” polymers and nonlinear trends between composition and function, highlighting the requirement for screening, with polymer aggregation being a key factor. The most active polymers reduced the volume of dimethyl sulfoxide (DMSO) required to cryopreserve a nucleated cell line, demonstrating the potential of this approach to identify materials for cell storage and transport

Characterization of Fiber-Forming Peptides and Proteins by Means of Atomic Force Microscopy

The atomic force microscope (AFM) is widely used in biological sciences due to its ability to perform imaging experiments at high resolution in a physiological environment, without special sample preparation such as fixation or staining. AFM is unique, in that it allows single molecule information of mechanical properties and molecular recognition to be gathered. This review sets out to identify methodological applications of AFM for characterization of fiber-forming proteins and peptides. The basics of AFM operation are detailed, with in-depth information for any life scientist to get a grasp on AFM capabilities. It also briefly describes antibody recognition imaging and mapping of nanomechanical properties on biological samples. Subsequently, examples of AFM application to fiber-forming natural proteins, and fiberforming synthetic peptides are given. Here, AFM is used primarily for structural characterization of fibers in combination with other techniques, such as circular dichroism and fluorescence spectroscopy. More recent developments in antibody recognition imaging to identify constituents of protein fibers formed in human disease are explored. This review, as a whole, seeks to encourage the life scientists dealing with protein aggregation phenomena to consider AFM as a part of their research toolkit, by highlighting the manifold capabilities of this technique

High Metallicity Mg II Absorbers in the z < 1 Lyman alpha Forest of PKS 0454+039: Giant LSB Galaxies?

We report the discovery of two iron-group enhanced high-metallicity Mg II absorbers in a search through 28 Lyman Alpha forest clouds along the PKS 0454+039 sight line. Based upon our survey and the measured redshift number densities of W_r(MgII) <= 0.3 A absorbers and Lyman Alpha absorbers at z ~ 1, we suggest that roughly 5% of Lyman Alpha absorbers at z < 1 will exhibit "weak" Mg II absorption to a 5-sigma W_r(2796) detection limit of 0.02 A. The two discovered absorbers, at redshifts z = 0.6248 and z = 0.9315, have W_r(Lya) = 0.33 and 0.15 A, respectively. Based upon photoionization modeling, the H I column densities are inferred to be in the range 15.8 <= log N(HI) <= 16.8 cm^-2. For the z = 0.6428 absorber, if the abundance pattern is solar, then the cloud has [Fe/H] > -1; if its gas-phase abundance follows that of depleted clouds in our Galaxy, then [Fe/H] > 0 is inferred. For the z = 0.9315 absorber, the metallicity is [Fe/H] > 0, whether the abundance pattern is solar or suffers depletion. Imaging and spectroscopic studies of the PKS 0454+039 field reveal no candidate luminous objects at these redshifts. We discuss the possibility that these Mg II absorbers may arise in the class of "giant" low surface brightness galaxies, which have [Fe/H] >= -1, and even [Fe/H] >= 0, in their extended disks. We tentatively suggest that a substantial fraction of these "weak" Mg II absorbers may select low surface brightness galaxies out to z ~ 1.Comment: Accepted The Astrophysical Journal; 25 pages; 6 encapsulated figure

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels, uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels