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By Modeling

The earliest ball-and-stick models were exactly that wooden balls in which holes were drilled to accommodate dowels that connected the atoms. Plastic versions, including relatively inexpensive student sets, became available in the 1960s and proved to be a valuable learning aid. Precisely scaled stainless steel framework and plastic space-filling models, although relatively expensive, were standard equipment in most research laboratories. [Pg.27]

All of the models, those you make yourself and those already provided on Learning By Modeling, can be viewed in different formats and rotated in three dimensions. [Pg.28]

As you go through this text, you will see two different modeling icons. The SpartanBuiid icon alerts you to a model-building opportunity, the SpartanView icon indicates that the Learning By Modeling CD includes a related model or animation. [Pg.28]

FIGURE 1.8 A wedge-and-dash drawing of the structure of methane. A solid wedge projects from the plane of the paper toward you a dashed wedge projects away from you. A bond represented by a line drawn in the customary way lies in the plane of the paper. [Pg.28]

SAMPLE SOLUTION (a) The structure shown accounts for all the electrons in hydrogen cyanide. There are no unshared electron pairs associated with carbon, and so the structure is determined by maximizing the separation between its single bond to hydrogen and the triple bond to nitrogen. Hydrogen cyanide is a linear molecule. [Pg.28]

Immediately preceding the Glossary at the back of this text is a tutorial showing you how to use Spar-tanBuild and SpartanView, and describing some additional features. [Pg.28]


Many simulations attempt to determine what motion of the polymer is possible. This can be done by modeling displacements of sections of the chain, Monte Carlo simulations, or reptation (a snakelike motion of the polymer chain as it threads past other chains). These motion studies ultimately attempt to determine a correlation between the molecular motion possible and the macroscopic flexibility, hardness, and so on. [Pg.311]

FIGURE 1 3 Boundary surfaces of the 2p orbitals The wave function changes sign at the nucleus The two halves of each orbital are indicated by different colors The yz plane is a nodal surface for the Ip orbital The probability of finding a electron in the yz plane is zero Anal ogously the xz plane is a nodal surface for the 2py orbital and the xy plane is a nodal surface for the 2pz orbital You may examine different presentations of a 2p orbital on Learning By Modeling... [Pg.9]

Accompanying this text is a CD entitled Learning By Modeling As its name implies it is a learning tool designed to help you better understand molecular structure and properties and contains two major components... [Pg.29]

SpartanView software with which you can browse through an archive of already prepared models on the Learning By Modeling CD These models include many of the same substances that appear m this text SpartanView is the tool you will use to view... [Pg.29]

Examine the models of am monia and pyridine on your Learning By Modeling CD Are the calculated charges on nitrogen consistent with their relative basicities What about their electro static potential maps ... [Pg.38]

Practice working with your Learning By Modeling software Construct molecular models of ethane ethylene and acetylene and compare them with respect to their geometry bond angles and C—H and C—C bond distances... [Pg.56]

Use your Learning By Modeling software to reproduce the models shown in Figure 2 7 so that you can better view their three dimensional shapes... [Pg.63]

Molecular models such as the one shown often do not explicitly show double and triple bonds Write a Lewis structure for this hydrocarbon showing the location of any multiple bonds Specify the hybndization state of each carbon (You can view this model in more detail on Learn mg By Modeling)... [Pg.103]

The animation on Learning By Model mg shows rotation about the C—C bond in ethane... [Pg.107]

In keeping with the bent bond de scription of Figure 3 10 the carbon-carbon bond distance in cycio propane (151 pm) is slightly shorter than that of ethane (153 pm) and cyclohexane (154 pm) The calculated val ues from molecular models (see Learning By Modeling) reproduce these experimen tal values... [Pg.114]

The best way to un derstand ring flip ping in cyclohexane IS to view the animation of Figure 3 18 in Learning By Modeling... [Pg.119]

Use Learning By Modeling to compare the calculated dipole moments of ethylene propene vinyl chloride and trans 1 chloropropene Unlike measured dipole moments the calculated ones do show the direction of the dipole moment How do the directions of the calculated dipole moments compare with those deduced by experimenf ... [Pg.197]

Which one of the following has the largest dipole moment (is the most polar)" Compare your answer with the calculated dipole moments on Learning By Modeling... [Pg.225]

Examine the electrostatic potential map of H3B THE (borane-tetrahydrofuran complex) on Learning By Modeling How does the electrostatic potential of the hydrogens bonded to boron dif fer from the potential of the hydrogens of the tetrahydrofuran ring" ... [Pg.280]

Learning By Modeling contains a model of dynemicin A which shows that the C=C—C=C—C=C unit can be incorporated into the molecule without much angle strain... [Pg.368]

Examine the models of 1 3 butadiene in Figure 10 6 on Learn mg By Modeling and com pare space filling models of the s CIS and s trans confor mation... [Pg.401]

Use Learning By Modeling to view the charge distribu tion in the allylic carbocation shown in the equation... [Pg.406]

Identify the longest and the shortest carbon-carbon bond in styrene and check your answer by making a minimized molecular model using Learning By Modeling... [Pg.472]

Which one of the dichlorobenzene isomers does not have a dipole moment" Which one has the largest dipole moment" Compare your answers with the dipole moments calculated using the molecular modeling software in Learning By Modeling... [Pg.472]

Examine the molecu lar models of toluene and (trifluro methyl)benzene on Learning By Modeling In which mole cule IS the electrostatic po tential of the ring most negative How should this affect the rate of nitration ... [Pg.488]

All of the calculated vibrational frequen cies given on Learn mg By Modeling are too high For example the C=C stretching frequency of 1 hexene observed at 1640 cm IS calculated to be at 1857 cm" ... [Pg.561]

The first page of this chapter displayed an electrostatic poten tial map of ferrocene You may wish to view a molecular model of it on Learning By Modeling... [Pg.609]

Which compound m each of the following pairs would you expect to have the more polar carbon-metal bond" Compare the models on Learning By Modeling with respect to the charge on the carbon bonded to the metal... [Pg.618]

Examine the molecular model of ferrocene on Learning By Modeling Does ferrocene have a dipole moment Would you expect the cyclopentadienyl nngs of ferrocene to be more reactive toward nucleophiles or electrophiles Where is the region of highest electrostatic potential... [Pg.622]

Compare the distri button of charge wCjjl among the atoms in CH4 BH4 and AIH4 on Learning By Modeling No tice how different they are... [Pg.628]

Use Learning By Modeling to make models of water methanol dimethyl ether and di tert butyl ether Mini mize their geometries and examine what happens to the C—O—C bond angle Compare the C—O bond dis tances in dimethyl ether and di tert butyl ether... [Pg.667]

View molecular models of dimethyl ether and ethylene oxide on Learning By Modeling Which one has the greater dipole moment Do the calculated dipole moments bear any relation ship to the observed boiling points (ethylene oxide +10°C dimethyl ether —25°C) d... [Pg.700]


See other pages where By Modeling is mentioned: [Pg.39]    [Pg.1770]    [Pg.2365]    [Pg.2679]    [Pg.198]    [Pg.28]    [Pg.102]    [Pg.105]    [Pg.106]    [Pg.112]    [Pg.120]    [Pg.285]    [Pg.369]    [Pg.409]    [Pg.445]    [Pg.472]    [Pg.525]    [Pg.622]    [Pg.664]    [Pg.686]   


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Building confidence in the mathematical models by calibration with a T-H-M field experiment

By Use of Model Potentials

Calculation by Rouse model

Calculation by reptation model

Calculation of Conversion by the Dispersion Model

Case studies of QSPRs obtained by linear modeling

Case study prediction of permeate flux decay during ultrafiltration performed in pulsating conditions by a hybrid neural model

Case study prediction of permeate flux decay during ultrafiltration performed in pulsating conditions by a neural model

Catalysis by micelles, membranes and other aqueous aggregates as models of enzyme

Catalysis by micelles, membranes and other aqueous aggregates as models of enzyme action

Catalytic Reaction Studies with Model Catalysts Made by Colloidal Lithography

Complete-Mixing Model for Gas Separation by Membranes

Composition profiles in the synthesis of MTBE obtained by a multilevel modeling approach

Computing Optimal Weights by Linear Programming Model

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Cross-Flow Model for Gas Separation by Membranes

Data Analysis by Modelling

Discrimination of Rival Models by Posterior Probability

Electrolyte-NRTL Model by Chen

Enzyme action, catalysis by micelles, membranes and other aqueous aggregates models

Estimation of Parameters by Inverse Modelling

Extracting Protection Factors by HX Modeling

Fabrication of Supported Model Catalysts by Lithography

General Closed Solution by Combined Model (Approximation)

Interaction by exchange with the mean model

Interpretation of Response Data by the Dispersion Model

Kinetic Model Equations for Inhibition by Substrates and Products

Kinetic modelling at electrodes modified by films

Large Analyte Ions such as Dendrimers and Proteins are Most Probably Produced by the Charged Residue Model (CRM)

Linear modeling by best subset selection

Linear modeling by stepwise subset selection

Micelles, membranes and other aqueous aggregates, catalysis by, as models

Micelles, membranes and other aqueous aggregates, catalysis by, as models enzyme action

Model Study via Chelation Control in the Aldol Reaction by Kalesse

Model building by homology

Modeling SOFC Fueled by Bio-Fuels

Modeling and Prediction of Solid Solubility by GE Models

Modeling of Process Intensification. Edited by F. J. Keil

Modelling of the reaction mechanisms by standard kinetic systems

Models based on positive feedback by cdc2 kinase

Models of Chiral Recognition by Cyclodextrins

Multiscale Modeling and Coarse Graining of Polymer Dynamics Simulations Guided by Statistical Beyond-Equilibrium Thermodynamics

Parameters of a Model by the Steepest Slope Method

Phase Optimization by TLC Following the PRISMA Model

Physical Inorganic Chemistry: Principles, Methods, and Models Edited by Andreja Bakac

Polymerization Events Modeled by Langevin Dynamics

Polymerization Kinetics Modeled by the Chemical Stochastic Equation

Prediction of Plasma and Tissue Concentration-Time Profiles by Using the PBPK Modeling Approach

Preparation of Model Polymer Colloids by Emulsion Polymerization

Principle of Dual-Beam LDA Explained by the Fringe Model

Response predicted by the model

Soft independent modeling by class

Soft independent modeling by class analogy

Soft independent modeling by class analogy SIMCA)

Stochastic Models by Probability Balance

Tackling stiffness in process simulations by modifications to the model

Testing the model by mixed tasks in patients with monohemispheric brain lesion

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