Framboids

2021 ◽  
Author(s):  
David Rickard
Keyword(s):  
Free Energy ◽  
Organic Matter ◽  
Brownian Motion ◽  
Natural Waters ◽  
Average Diameter ◽  
Surface Forces ◽  
Self Organization ◽  
Observable Universe ◽  
Entropy Maximization ◽  
Supersaturated Solutions

Framboids may be the most astonishing and abundant natural features you have never heard of. These microscopic spherules of golden pyrite consist of thousands of even smaller microcrystals, often arranged in stunning geometric arrays. There are probably 1030 on Earth, and they are forming at a rate of 1020 every second. This means that there are a billion times more framboids than sand grains on Earth, and a million times more framboids than stars in the observable universe. They are all around us: they can be found in rocks of all ages and in present-day sediments, soils, and natural waters. The sulfur in the pyrite is mainly produced by bacteria, and many framboids contain organic matter. They are formed through burst nucleation of supersaturated solutions of iron and sulfide, followed by limited crystal growth in diffusion-dominated stagnant sediments. The framboids self-assemble as surface free energy is minimized and the microcrystals are attracted to each other by surface forces. Self-organization occurs through entropy maximization, and the microcrystals rotate into their final positions through Brownian motion. The final shape of the framboids is often actually polygonal or partially facetted rather than spherical, as icosahedral microcrystal packing develops. Their average diameter is around 6 microns and the average microcrystal size is about 0.1 microns. There is no significant change in these dimensions with time: the framboid is an exceptionally stable structure, and the oldest may be 2.9 billion years old. This means that they provide samples of the chemistry of ancient environments.

scholarly journals Photochemical effects on the interaction of enzymes and dissolved organic matter in natural waters

2003 ◽  
Vol 48 (5) ◽  
pp. 1818-1824 ◽  
Author(s):  
Norman M. Scully ◽  
Lars J. Tranvik ◽  
William J. Cooper
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Natural Waters

scholarly journals SELF-ORGANIZATION IN COMPLEX SYSTEMS AS DECISION MAKING

2014 ◽  
Vol 17 (03n04) ◽  
pp. 1450016 ◽  
Author(s):  
V. I. YUKALOV ◽  
D. SORNETTE
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Mathematical Formulation ◽  
Conditional Entropy ◽  
Self Organization ◽  
Entropy Maximization ◽  
Natural Systems ◽  
Macroscopic States ◽  
System States ◽  
Statistical Systems

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

scholarly journals Optical Properties of Dissolved Organic Matter in Finnish and Estonian Lakes

2003 ◽  
Vol 34 (4) ◽  
pp. 361-386 ◽  
Author(s):  
L. Sipelgas ◽  
H. Arst ◽  
K. Kallio ◽  
A. Erm ◽  
P. Oja ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Natural Waters ◽  
Mean Value ◽  
Published Data ◽  
Optical Parameters ◽  
Exponential Approximation ◽  
Absorption Coefficients ◽  
Specific Absorption ◽  
The Mean

The main objective of the present study is to test various methods for describing the absorption spectra of coloured dissolved organic matter (CDOM) and to determine the numerical values of some optical parameters of CDOM in lakes with diverse water quality. First, the parameters of an exponential model in different spectral intervals were determined. In addition, the suitability of some other models for the approximation of CDOM spectra was estimated. Specific absorption coefficients of CDOM were calculated from the absorption coefficients and dissolved organic carbon (DOC) concentrations. The experimental initial data were differences between spectral attenuation coefficients of filtered and distilled water. Two datasets were used: 1) for 13 Estonian and 7 Finnish lakes (altogether 404 spectra between 350 and 700 nm) measured by the Estonian Marine Institute (EMI); 2) for 10 Finnish lakes (73 spectra) measured by the Finnish Environment Institute (FEI). The spectra of CDOM absorption coefficients (aCDOM) were calculated from experimental data taking into account the correction due to scattering properties of colloids in the filtered water. The total content of CDOM in natural waters of Estonian and Finnish lakes was expressed by means of aCDOM at the wavelength of 380 nm. It varied significantly, from 0.71 to 19.5 m−1, the mean value (of all the investigated lakes) being around 6.6 m−1. Slopes of the exponential approximation varied widely, from 0.006 to 0.03 nm−1. Averaged over all lakes values of slope for the interval 380-500 nm obtained from the EMI dataset are close to those obtained from the FEI dataset: from 0.014 nm−1 (without correction) to 0.016-0.017 nm-1 (with different types of correction). These results are in good correspondence with most published data. Attempts to describe the spectra in the region of 350-700 nm by means of hyperexponential functions (∽ exp(-αλη)) show that: (1) η < 1 (in the case of traditional exponential approximation η = 1); (2) a promising idea is to seek the best fit only for wavelengths λ > λ1, where λ1 will be chosen taking into account the real shape of aCDOM spectra. The mean value of the specific absorption coefficient (a*CDOM) at the wavelength 380 nm obtained in this study (0.44 L mg−1 m−1) is close to the values published in the literature, if we assume that a*CDOM (380) is calculated using the data of dissolved organic matter (DOM). The optically non-active fraction of DOM in our study was high and therefore a*CDOM (380) was considerably higher (1.01 L mg−1 m−1) than a*CDOM (380). The results of the present work could be used in the modeling of underwater light field as well as in the interpretation of radiation measurements and optical remote sensing results.

scholarly journals Serotonergic Axons as Fractional Brownian Motion Paths: Insights into the Self-organization of Regional Densities

2019 ◽  
Author(s):  
Skirmantas Janušonis ◽  
Nils Detering ◽  
Ralf Metzler ◽  
Thomas Vojta
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Adult Brain ◽  
The Self ◽  
Self Organization ◽  
Dense Matrix ◽  
Two Dimensional ◽  
Wide Range ◽  
Motion Paths ◽  
Key Features

ABSTRACTAll vertebrate brains contain a dense matrix of thin fibers that release serotonin (5-hydroxytryptamine), a neurotransmitter that modulates a wide range of neural, glial, and vascular processes. Perturbations in the density of this matrix have been associated with a number of mental disorders, including autism and depression, but its self-organization and plasticity remain poorly understood. We introduce a model based on reflected Fractional Brownian Motion (FBM), a rigorously defined stochastic process, and show that it recapitulates some key features of regional serotonergic fiber densities. Specifically, we use supercomputing simulations to model fibers as FBM-paths in two-dimensional brain-like domains and demonstrate that the resultant steady state distributions approximate the fiber distributions in physical brain sections immunostained for the serotonin transporter (a marker for serotonergic axons in the adult brain). We suggest that this framework can support predictive descriptions and manipulations of the serotonergic matrix and that it can be further extended to incorporate the detailed physical properties of the fibers and their environment.

Complexation of Dissolved Organic Matter with Trace Metal Ions in Natural Waters

2012 ◽  
pp. 769-849 ◽  
Author(s):  
Khan M. G. Mostofa ◽  
Cong-qiang Liu ◽  
Xinbin Feng ◽  
Takahito Yoshioka ◽  
Davide Vione ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Trace Metal ◽  
Metal Ions ◽  
Natural Waters ◽  
Trace Metal Ions

scholarly journals Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria

2019 ◽  
Vol 116 (46) ◽  
pp. 23309-23316 ◽  
Author(s):  
Ali Ebrahimi ◽  
Julia Schwartzman ◽  
Otto X. Cordero
Keyword(s):  
Organic Matter ◽  
Public Goods ◽  
Social Interactions ◽  
Particulate Organic Matter ◽  
High Efficiency ◽  
Cell Aggregation ◽  
Self Organization ◽  
Turnover Rates ◽  
Particulate Carbon ◽  
Hydrolysis Of

The recycling of particulate organic matter (POM) by microbes is a key part of the global carbon cycle. This process is mediated by the extracellular hydrolysis of polysaccharides, which can trigger social behaviors in bacteria resulting from the production of public goods. Despite the potential importance of public good-mediated interactions, their relevance in the environment remains unclear. In this study, we developed a computational and experimental model system to address this challenge and studied how the POM depolymerization rate and its uptake efficiency (2 main ecosystem function parameters) depended on social interactions and spatial self-organization on particle surfaces. We found an emergent trade-off between rate and efficiency resulting from the competition between oligosaccharide diffusion and cellular uptake, with low rate and high efficiency being achieved through cell-to-cell cooperation between degraders. Bacteria cooperated by aggregating in cell clusters of ∼10 to 20 µm, in which cells were able to share public goods. This phenomenon, which was independent of any explicit group-level regulation, led to the emergence of critical cell concentrations below which degradation did not occur, despite all resources being available in excess. In contrast, when particles were labile and turnover rates were high, aggregation promoted competition and decreased the efficiency of carbon use. Our study shows how social interactions and cell aggregation determine the rate and efficiency of particulate carbon turnover in environmentally relevant scenarios.

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