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1. Efficient removal of short-chain and long-chain PFAS by cationic nanocellulose

   https://doi.org/10.1039/d3ta01851b  

   Li, Duning; Lee, Cheng-Shiuan; Zhang, Yi; Das, Rasel; Akter, Fahmida; Venkatesan, Arjun K.; Hsiao, Benjamin S. (May 2023, Journal of Materials Chemistry A)

   Although most manufacturers stopped using long-chain per- and polyfluoroalkyl substances (PFASs), including perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), short-chain PFASs are still widely employed. Short-chain PFASs are less known in terms of toxicity and have different adsorption behavior from long-chain PFASs. Previous studies have shown electrostatic interaction with the adsorbent to be the dominant mechanism for the removal of short-chain PFASs. In this study, we designed a high charge density cationic quaternized nanocellulose (QNC) to enhance the removal of both short- and long-chain PFASs from contaminated water. Systematic batch adsorption tests were conducted using the QNC adsorbent to compare its efficiency against PFASs with varying chain lengths and functional groups. From the kinetic study, PFBA (perfluorobutanoic acid), PFBS (perfluorobutanesulfonic acid) and PFOS showed rapid adsorption rates, which reached near equilibrium values (>95% of removal) between 1 min to 15 min, while PFOA required a relatively longer equilibration time of 2 h (it obtained 90% of removal within 15 min). According to the isotherm results, the maximum adsorption capacity ( Q m ) of the QNC adsorbent exhibited the following trend: PFOS ( Q m = 559 mg g −1 or 1.12 mmol g −1 ) > PFOA ( Q m = 405 mg g −1 or 0.98 mmol g −1 ) > PFBS ( Q m = 319 mg g −1 or 1.06 mmol g −1 ) > PFBA ( Q m = 121 mg g −1 or 0.57 mmol g −1 ). This adsorption order generally matches the hydrophobicity trend among four PFASs associated with both PFAS chain length and functional group. In competitive studies, pre-adsorbed short-chain PFASs were quickly desorbed by long-chain PFASs, suggesting that the hydrophobicity of the molecule played an important role in the adsorption process on to QNC. Finally, the developed QNC adsorbent was tested to treat PFAS-contaminated groundwater, which showed excellent removal efficiency (>95%) for long-chain PFASs (C7–C9) even at a low adsorbent dose of 32 mg L −1 . However, short-chain PFASs ( i.e. , PFBA and perfluoropentanoic acid (PFPeA)) were poorly removed by the QNC adsorbent (0% and 10% removal, respectively) due to competing constituents in the groundwater matrix. This was further confirmed by controlled experiments that revealed a drop in the performance of QNC to remove short-chain PFASs at elevated ionic strength (NaCl), but not for long-chain PFASs, likely due to charge neutralization of the anionic functional group of PFASs by inorganic cations. Overall, the QNC adsorbent featured improved PFAS adsorption capacity at almost two-fold of PFAS removal by granular activated carbons, especially for short-chain PFASs. We believe, QNC can complement the use of common treatment methods such as activated carbon or ionic exchange resin to remove a wide range of PFAS pollutants, heading towards the complete remediation of PFAS contamination. 

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2. Importance of Nucleophilicity of Chain-Transfer Agents for Controlled Cationic Degenerative Chain-Transfer Polymerization

   https://doi.org/10.1021/acs.macromol.0c00290  

   Boeck, Parker Thomas; Tanaka, Joji; Liu, Shubin; You, Wei (June 2020, Macromolecules)
3. Faithful Chain-of-Thought Reasoning

   Lyu, Qing; Havaldar, Shreya; Stein, Adam; Zhang, Li; Rao, Delip; Wong, Eric; Apidianaki, Marianna; Callison-Burch, Chris (November 2023, The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023))

   While Chain-of-Thought (CoT) prompting boosts Language Models’ (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query → symbolic reasoning chain) and Problem Solving (reasoning chain → answer), using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP), 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA), and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them), showing a strong synergy between faithfulness and accuracy. 

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4. Polymer with Competing Depolymerization Pathways: Chain Unzipping versus Chain Scission

   https://doi.org/10.1021/acsmacrolett.0c00250  

   Addy, Partha Sarathi; Shivrayan, Manisha; Cencer, Morgan; Zhuang, Jiaming; Moore, Jeffrey S.; Thayumanavan, S. (June 2020, ACS Macro Letters)

   null (Ed.)
5. Max Markov Chain

   https://doi.org/10.24963/ijcai.2023/639  

   Zhang, Yu; Bucklew, Mitchell (August 2023, International Joint Conferences on Artificial Intelligence Organization)

   In this paper, we introduce Max Markov Chain (MMC), a novel model for sequential data with sparse correlations among the state variables.It may also be viewed as a special class of approximate models for High-order Markov Chains (HMCs).MMC is desirable for domains where the sparse correlations are long-term and vary in their temporal stretches.Although generally intractable, parameter optimization for MMC can be solved analytically.However, based on this result,we derive an approximate solution that is highly efficient empirically.When compared with HMC and approximate HMC models, MMCcombines better sample efficiency, model parsimony, and an outstanding computational advantage.Such a quality allows MMC to scale to large domainswhere the competing models would struggle to perform.We compare MMC with several baselines with synthetic and real-world datasets to demonstrate MMC as a valuable alternative for stochastic modeling. 

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