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Predicting Products: Substitution vs. Elimination02:52

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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
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Related Experiment Video

Updated: Sep 27, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
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Predicting emerging chemical content in consumer products using machine learning.

Luka Lila Thornton1, David E Carlson2, Mark R Wiesner1

  • 1Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708, USA; Center for the Environmental Implications of NanoTechnology (CEINT), USA.

The Science of the Total Environment
|April 11, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning can predict chemical concentrations in products when data is limited. Including functional use improved predictions for emerging nanomaterials, aiding safety assessments.

Keywords:
Artificial intelligenceChemical functionCheminformaticsConsumer product safetyEnvironmental exposureExposure modelingNanomaterialsNanotechnology

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Consumer product chemical ingredients are dynamic, necessitating accurate exposure and risk assessment.
  • Manufacturer-reported chemical weight fractions are often incomplete, hindering safety evaluations.
  • Emerging substances like engineered nanomaterials present unique data-gap challenges.

Purpose of the Study:

  • To assess machine learning (ML) utility for predicting chemical weight fractions in data-limited scenarios.
  • To develop and test predictive frameworks for both data-poor (nanomaterials) and data-rich (bulk chemicals) datasets.
  • To identify key features, such as functional use categories, that enhance predictive accuracy.

Main Methods:

  • Developed two ML frameworks: one for data-poor engineered nanomaterials and one for data-rich bulk chemicals.
  • Utilized feature variables including chemical properties, functional use, product categories, and data source.
  • Employed random forest and nonlinear support vector classifiers for predicting weight fraction bins.

Main Results:

  • Functional use data significantly improved predictive performance when combined with chemical properties.
  • Models achieved moderate success in stratifying material-product observations into order-of-magnitude weight fractions.
  • The best model reached an average balanced accuracy of 73% for nanomaterials product data.
  • A positive correlation was observed between sample size and predictive accuracy.

Conclusions:

  • Machine learning offers a promising approach for estimating chemical weight fractions in complex consumer products.
  • Functional use categories are valuable predictors, particularly for assessing emerging chemicals.
  • Continued investment in chemical data collection will further enhance the power of ML for chemical safety and sustainability assessments.