Steady state thermal radiometers

A radiometer is described operating in a vacuum under steady state conditions. The front element is an aluminum sheet painted on the outer side with black or other absorptive material of selected characteristics. A thermocouple is bonded to the inner side of the aluminum sheet. That is backed by highly insulative layers of glass fiber and crinkled, aluminized Mylar polyester. Those layers are backed with a sturdy, polyester sheet, and the entire lamination is laced together by nylon cords. The device is highly reliable in that it does not drift out of calibration, and is significantly inexpensive

Construction and Measurements of an Improved Vacuum-Swing-Adsorption Radon-Mitigation System

In order to reduce backgrounds from radon-daughter plate-out onto detector surfaces, an ultra-low-radon cleanroom is being commissioned at the South Dakota School of Mines and Technology. An improved vacuum-swing-adsorption radon mitigation system and cleanroom build upon a previous design implemented at Syracuse University that achieved radon levels of $\sim$0.2$\,$Bq$\,$m$^{-3}$. This improved system will employ a better pump and larger carbon beds feeding a redesigned cleanroom with an internal HVAC unit and aged water for humidification. With the rebuilt (original) radon mitigation system, the new low-radon cleanroom has already achieved a $>$$\,$300$\times$ reduction from an input activity of $58.6\pm0.7$$\,$Bq$\,$m$^{-3}$ to a cleanroom activity of $0.13\pm0.06$$\,$Bq$\,$m$^{-3}$.Comment: 5 pages, 4 figures, Proceedings of Low Radioactivity Techniques (LRT) 2015, Seattle, WA, March 18-20, 201

Methane-Oxidizing Seawater Microbial Communities from an Arctic Shelf

Marine microbial communities can consume dissolved methane before it can escape to the atmosphere and contribute to global warming. Seawater over the shallow Arctic shelf is characterized by excess methane compared to atmospheric equilibrium. This methane originates in sediment, permafrost, and hydrate. Particularly high concentrations are found beneath sea ice. We studied the structure and methane oxidation potential of the microbial communities from seawater collected close to Utqiagvik, Alaska, in April 2016. The in situ methane concentrations were 16.3 ± 7.2 nmol L−1 , approximately 4.8 times oversaturated relative to atmospheric equilibrium. The group of methaneoxidizing bacteria (MOB) in the natural seawater and incubated seawater was \u3e 97 % dominated by Methylococcales (γ -Proteobacteria). Incubations of seawater under a range of methane concentrations led to loss of diversity in the bacterial community. The abundance of MOB was low with maximal fractions of 2.5 % at 200 times elevated methane concentration, while sequence reads of non-MOB methylotrophs were 4 times more abundant than MOB in most incubations. The abundances of MOB as well as non-MOB methylotroph sequences correlated tightly with the rate constant (kox) for methane oxidation, indicating that non-MOB methylotrophs might be coupled to MOB and involved in community methane oxidation. In sea ice, where methane concentrations of 82 ± 35.8 nmol kg−1 were found, Methylobacterium (α-Proteobacteria) was the dominant MOB with a relative abundance of 80 %. Total MOB abundances were very low in sea ice, with maximal fractions found at the ice– snow interface (0.1 %), while non-MOB methylotrophs were present in abundances similar to natural seawater communities. The dissimilarities in MOB taxa, methane concentrations, and stable isotope ratios between the sea ice and water column point toward different methane dynamics in the two environments

Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits, the most significant of which is accuracy. Neural networks can inherently incorporate multi-body effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields even when accounting for specialized hardware which accelerates the training and integration of such networks. The second, and the focus of this paper, is the need for the considerable amount of data needed to train such force fields. It is common to use tens of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG models usefulness in the first place. As we investigate in this work, it is apparent that this data-hunger trap from neural networks for predicting molecular energies and forces is caused in large part by the difficulty in learning force equivariance, i.e., the fact that force vectors should rotate while maintaining their magnitude in response to an equivalent rotation of the system. We demonstrate that for CG water, networks that inherently incorporate this equivariance into their embedding can produce functional models using datasets as small as a single frame of reference data, which networks without inherent symmetry equivariance cannot

A Parameter Model of Gas Exchange for the Seasonal Sea Ice Zone

Carbon budgets for the polar oceans require better constraint on air–sea gas exchange in the sea ice zone (SIZ). Here, we utilize advances in the theory of turbulence, mixing and air–sea flux in the ice–ocean boundary layer (IOBL) to formulate a simple model for gas exchange when the surface ocean is partially covered by sea ice. The gas transfer velocity (k) is related to shear-driven and convection-driven turbulence in the aqueous mass boundary layer, and to the mean-squared wave slope at the air–sea interface. We use the model to estimate k along the drift track of ice-tethered profilers (ITPs) in the Arctic. Individual estimates of daily-averaged k from ITP drifts ranged between 1.1 and 22 m d−1, and the fraction of open water (f) ranged from 0 to 0.83. Converted to area-weighted effective transfer velocities (keff), the minimum value of keff was 10−55 m d−1 near f = 0 with values exceeding keff = 5 m d−1 at f = 0.4. The model indicates that effects from shear and convection in the sea ice zone contribute an additional 40% to the magnitude of keff, beyond what would be predicted from an estimate of keff based solely upon a wind speed parameterization. Although the ultimate scaling relationship for gas exchange in the sea ice zone will require validation in laboratory and field studies, the basic parameter model described here demonstrates that it is feasible to formulate estimates of k based upon properties of the IOBL using data sources that presently exist

Measurement in biological systems from the self-organisation point of view

Measurement in biological systems became a subject of concern as a consequence of numerous reports on limited reproducibility of experimental results. To reveal origins of this inconsistency, we have examined general features of biological systems as dynamical systems far from not only their chemical equilibrium, but, in most cases, also of their Lyapunov stable states. Thus, in biological experiments, we do not observe states, but distinct trajectories followed by the examined organism. If one of the possible sequences is selected, a minute sub-section of the whole problem is obtained, sometimes in a seemingly highly reproducible manner. But the state of the organism is known only if a complete set of possible trajectories is known. And this is often practically impossible. Therefore, we propose a different framework for reporting and analysis of biological experiments, respecting the view of non-linear mathematics. This view should be used to avoid overoptimistic results, which have to be consequently retracted or largely complemented. An increase of specification of experimental procedures is the way for better understanding of the scope of paths, which the biological system may be evolving. And it is hidden in the evolution of experimental protocols.Comment: 13 pages, 5 figure