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Related Concept Videos

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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Updated: Jun 20, 2026

Solid-phase Submonomer Synthesis of Peptoid Polymers and their Self-Assembly into Highly-Ordered Nanosheets
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A systematic methodology to develop bottom-up coarse-grained models for sequence-specific polypeptoids.

Daniela M Rivera Mirabal1, Sally Jiao1, Shawn D Mengel1

  • 1Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California 93106, USA.

The Journal of Chemical Physics
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a physics-based simulation workflow to predict how sequence impacts polymer structure and properties. This method enables in silico screening of sequence-defined polymers, overcoming limitations of experimental high-throughput screening.

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Chemistry

Background:

  • Sequence-controlled polymers offer tunable properties but face challenges in high-throughput screening.
  • The vast chemical design space of polypeptoids is limited by a lack of large structural and property databases.
  • Predictive models for polypeptoid behavior are needed to guide material design.

Purpose of the Study:

  • To develop a systematic, physics-based computational method for predicting how sequence influences polypeptoid structure and material properties.
  • To create a multiscale simulation workflow for bottom-up coarse-grained (CG) peptoid modeling.
  • To enable in silico screening of sequence-defined polymers.

Main Methods:

  • Developed a multiscale simulation workflow using the relative entropy approach for bottom-up coarse-grained (CG) peptoid model development.
  • Created a library of peptoid monomers for simulating a wide range of sequences in long-chain and multi-chain systems.
  • Validated CG models against all-atom simulations and experimental measurements (double electron-electron resonance spectroscopy).

Main Results:

  • Successfully created validated bottom-up coarse-grained peptoid models.
  • Demonstrated the workflow's ability to navigate the vast sequence and chemistry space of sequence-defined polymers.
  • Provided molecular-level insights into sequence-structure-property relationships.

Conclusions:

  • The developed physics-based simulation approach offers a framework for understanding and predicting the behavior of sequence-defined polymers.
  • This method facilitates in silico screening, addressing limitations in experimental high-throughput synthesis and data availability.
  • Enables efficient exploration of the chemical design space for novel peptoid-based materials.