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Related Concept Videos

Nucleophilic Aromatic Substitution: Addition–Elimination (SNAr)01:30

Nucleophilic Aromatic Substitution: Addition–Elimination (SNAr)

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Nucleophilic substitution in aromatic compounds is feasible in substrates bearing strong electron-withdrawing substituents positioned ortho or para to the leaving group. The reaction proceeds via two steps: the addition of the nucleophile and the elimination of the leaving group.
The reaction begins with an attack of the nucleophile on the carbon that holds the leaving group. This results in the delocalization of the π electrons over the ring carbons. The resonance interaction between...
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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 Aromatic Substitution: Elimination–Addition01:11

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Simple aryl halides do not react with nucleophiles. However, nucleophilic aromatic substitutions can be forced under certain conditions, such as high temperatures or strong bases. The mechanism of substitution under such conditions involves the highly unstable and reactive benzyne intermediate. Benzyne contains equivalent carbon centers at both ends of the triple bond, each of which is equally susceptible to nucleophilic attack. This 50–50 distribution of products is...
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Electrophilic Aromatic Substitution: Overview01:16

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In an electrophilic aromatic substitution reaction, an electrophile substitutes for a hydrogen of an aromatic compound.
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Nucleophilic Substitution Reactions02:34

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Historical perspective
In 1896, the German chemist Paul Walden discovered that he could interconvert pure enantiomeric (+) and (-) malic acids through a series of reactions. This conversion suggested the involvement of optical inversion during the substitution reaction. Further, in 1930, Sir Christopher Ingold described for the first time two different forms of nucleophilic substitution reactions, which are known as SN1 (nucleophilic substitution unimolecular) and SN2 (nucleophilic substitution...
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Nucleophilic Aromatic Substitution of Aryldiazonium Salts: Aromatic SN101:14

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Treating arylamines with nitrous acid gives aryldiazonium salts that are effective substrates in nucleophilic aromatic substitution reactions. The diazonio group in these salts can be easily displaced by different nucleophiles, yielding a wide variety of substituted benzenes. The leaving group departs as nitrogen gas, and this easy elimination is the driving force for the substitution reaction.
In the Sandmeyer reaction, for example, the diazonio group is replaced by a chloro, bromo,...
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Updated: Mar 27, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Multiobjective Simulated Annealing-Based Stopwords Substitution for Rubbish Text Attack.

Chen Li, Xinghao Yang, Ao Wang

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    Summary
    This summary is machine-generated.

    Modern natural language processing (NLP) models are vulnerable to nonsensical text, or rubbish examples. Our new method, MOSA-S2, generates diverse rubbish examples to better evaluate and improve NLP model robustness.

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    Related Experiment Videos

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    Area of Science:

    • Natural Language Processing (NLP)
    • Artificial Intelligence (AI)
    • Machine Learning (ML)

    Background:

    • Modern NLP models are sensitive to adversarial examples but insensitive to text rubbish examples.
    • Text rubbish examples are nonsensical sentences that do not change model predictions, crucial for robustness.
    • Existing methods for generating rubbish examples use single-objective optimization and limited modification strategies, leading to local optima and lack of diversity.

    Purpose of the Study:

    • To propose a novel algorithm, MOSA-S2 (multiobjective simulated annealing-based stopword substitution), for generating diverse and high-quality text rubbish examples.
    • To address the limitations of existing methods by employing multiobjective optimization and advanced text modification strategies.
    • To enhance the evaluation, improvement, and interpretation of NLP model robustness.

    Main Methods:

    • Developed MOSA-S2, which replaces input words with meaningless stopwords using importance-based composite perturbation for enhanced rubbish sample generation.
    • Implemented a multiobjective simulated annealing method to adaptively prioritize word replacements, escaping local optima and balancing conflicting objectives via Pareto dominance.
    • Introduced a grammatically constrained variant to improve rubbish text readability while maximizing semantic deviation from the original.

    Main Results:

    • Evaluated MOSA-S2 on six text datasets against seven popular neural models, demonstrating superior effectiveness and efficiency.
    • Generated rubbish examples that maintained model predictions with even higher confidence, indicating a potential lack of full semantic comprehension in NLP models.
    • Showcased the ability of MOSA-S2 to produce diverse and high-quality rubbish samples, outperforming existing generation techniques.

    Conclusions:

    • The MOSA-S2 algorithm effectively generates diverse and high-quality text rubbish examples, significantly advancing NLP model robustness evaluation.
    • Findings suggest that current NLP models may not fully grasp textual semantics, as evidenced by their consistent predictions on nonsensical inputs.
    • The proposed method offers a promising direction for improving the reliability and interpretability of NLP systems.