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Empirical formula

In chemistry, the empirical formula of a chemical compound is the simplest whole number ratio of atoms present in a compound. A simple example of this concept is that the empirical formula of sulfur monoxide, or SO, would simply be SO, as is the empirical formula of disulfur dioxide, S2O2. Thus, sulfur monoxide and disulfur dioxide, both compounds of sulfur and oxygen, have the same empirical formula. However, their molecular formulas, which express the number of atoms in each molecule of a chemical compound, are not the same. An empirical formula makes no mention of the arrangement or number of atoms. It is standard for many ionic compounds, like calcium chloride (CaCl2), and for macromolecules, such as silicon dioxide (SiO2). The molecular formula, on the other hand, shows the number of each type of atom in a molecule. The structural formula shows the arrangement of the molecule. It is also possible for different types of compounds to have equal empirical formulas. In the early d

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Pizgrischite, (Cu,Fe)Cu14PbBi17S35, a new sulfosalt from the Swiss Alps: Description, crystal structure and occurrence

Kurt Schenk

Pizgrischite, (Cu,Fe)Cu14PbBi17S35, is a new mineral species named after the type locality, Piz Grisch Mountain, Val Ferrera, Graubunden, Switzerland. This sulfosalt occurs as thin, striated, metallic lead-grey blades measuring up to I cm in length, embedded in quartz and associated with tetrahedrite, chalcopyrite, pyrite, sphalerite, emplectite and derivatives of the aikinite-bismuthinite series. In plane-polarized light, the new species is brownish grey with no perceptible pleochroism; under crossed nicols in oil immersion, it presents a weak anisotropy with dark brown tints. Minimum and maximum reflectance values (in %) in air are: 40.7-42.15 (470 nm), 41.2-43.1 (546 nm), 41.2-43.35 (589 nm) and 40.7-43.3 (650 nm). Cleavage is perfect along 001 I and well developed on {010}. Abundant polysynthetic twinning is observed on (010). The mean micro-indentation hardness is 190 kg/mm(2) (Mohs hardness 3.3), and the calculated density is 6.58 g/cm(3). Electron-microprobe analyses yield (wt%; mean result of seven analyses): Cu 16.48, Pb 2.10, Fe 0.77, Bi 60.70, Sb 0.35, S 19.16, Se 0.04, total 99.60. The resulting empirical chemical formula is (Cu15.24Fe0.80Pb0.60)(Sigma 16.64)(Bi17.07Sb0.17)(Sigma 17.24)(S35.09Se0.03)(Sigma 35.12), in accordance with the formula derived from the single-crystal refinement of the structure, (Cu,Fe)Cu14PbBi17S35. Pizgrischite is monoclinic, space group C2/m, with the following unit-cell parameters: a 35.054(2), b3.91123(I), c43.192(2) angstrom, beta 96.713(4)degrees, V5881.24 angstrom(3), Z=4. The strongest seven X-ray powder-diffraction lines [d in angstrom (I)(hkl)] are: 5.364(40)((6) over bar 04), 4.080(50)((8) over bar 05), 3.120(40)(118), 3.104(68)((3) over bar 18), 2.759(53) ((9) over bar 11),2.752(44)(910) and 1.956(100)(020). The crystal structure is an expanded monoclinic derivative of kupcikite. Pizgrischite belongs to the cuprobismutite series of bismuth sulfosalts but, sensu stricto, it is not a homologue of cuprobismutite. At the type locality. pizarischite is the result of the Alpine metamorphism under greenschist-facies conditions of pre-Tertiary hydrothermal Cu-Bi mineralization.

Mineralogical Association of Canada2007

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A prominent parameter in dealing with swash and morphological evolution is the runup length or height, defined as the limit of landward sea. Therefore, it is necessary to predict the runup height in this area. In this paper, the abilities of a new Adaptive-Network-Based Fuzzy Inference System (ANFIS) using subtractive fuzzy clustering method, Fuzzy Inference System (FIS), and existing empirical formulas are implemented for predicting and modeling wave runup in the swash zone. The ANFIS and FIS models are established using the slope angle; Iribarren number and antecedent wave runup data. The empirical formulas are also applied to the same data. Statistical measures were used to evaluate the performance of the models. The existing wave runup, bottom slope, and deep water Iribarren number data for regular and irregular waves on smooth, impermeable plane slopes were used as case studies. The comparison of results reveals that, the ANFIS model provides high accuracy and reliability for wave runup estimation, providing better predictions compared to other techniques. The paper demonstrates that the neurofuzzy approach developed is a good trade-off between the advantages of the Neural Network (supervised learning capabilities) and of the Fuzzy Logic (knowledge which can be explained and understood) without the generic drawbacks (overfitting, optimization problems, etc.) typical in other approaches.

2008

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Learning in Volatile Environments With the Bayes Factor Surprise

Johanni Michael Brea, Wulfram Gerstner, Vasiliki Liakoni, Alireza Modirshanechi

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The modulation depends on a probability ratio, which we call the Bayes Factor Surprise, that tests the prior belief against the current belief. We demonstrate that in several existing approximate algorithms, the Bayes Factor Surprise modulates the rate of adaptation to new observations. We derive three novel surprise-based algorithms, one in the family of particle filters, one in the family of variational learning, and one in the family of message passing, that have constant scaling in observation sequence length and particularly simple update dynamics for any distribution in the exponential family. Empirical results show that these surprise-based algorithms estimate parameters better than alternative approximate approaches and reach levels of performance comparable to computationally more expensive algorithms. The Bayes Factor Surprise is related to but different from the Shannon Surprise. In two hypothetical experiments, we make testable predictions for physiological indicators that dissociate the Bayes Factor Surprise from the Shannon Surprise. The theoretical insight of casting various approaches as surprise-based learning, as well as the proposed online algorithms, may be applied to the analysis of animal and human behavior and to reinforcement learning in nonstationary environments.

MIT PRESS2021

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Formule chimique

thumb|Formule développée plane de l'isobutane. La formule chimique d'un composé chimique, d'une espèce chimique ou d'un minéral est une expression indiquant quels éléments, et dans quelles proportion

Soufre

Le soufre est l'élément chimique de numéro atomique 16, de symbole S. C'est un membre du groupe des chalcogènes. C'est un non-métal multivalent abondant, insipide, et insoluble dans l'eau. Le soufr

Acide acétique

L'acide éthanoïque ou acide acétique est un acide carboxylique avec une chaîne carbonée théorique en C2, analogue à l'éthane, de formule semi-développée CH3-CO-OH ou courte AcOH, où Ac signifie « CH

CH-223: Organometallic chemistry

Basic organometallic chemistry will be covered in this course.

Structure and bonding in organometallic compounds.
reactivity of organometallic compounds, stoichiometric reactions, catalyzed reactions and reaction mechanisms.

CH-160(en): Advanced general chemistry (english)

This course aims to teach essential notions of the structure of matter, chemical equilibria and reactivity. Classes and exercises provide the means to analyze and solve, by reasoning and calculation, novel problems of geeneral chemistry.

MSE-425: Soft matter

The first part of the course is devoted to the self-assembly of molecules. In the second part we discuss basic physical chemical principles of polymers in solutions, at interfaces, and in bulk. Finally, we look at colloids and emulsions.