Evidence map›Paper›PMID 42577972›Full record

ArticleACS omega2026

Data-Efficient Mapping of Copolymerization Curves and Reactivity Ratios via Gradient Flow Polymerization, Inline ATR-FTIR, and Lasso Regression.

Araki Wakiuchi, Aniruddha Nag, Swarit Jasial, Masashi Nakayama, Shogo Takasuka, Shigehito Asano, Ryo Hashizume, Miho Hatanaka, Yu-Ya Ohnishi, Takamitsu Matsubara and 5 more

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Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Araki WakiuchiJSR Corporation, 3-103-9 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-0821, Japan.
Aniruddha NagGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0002-1570-2262
Swarit JasialGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0003-1415-8151
Masashi NakayamaGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
Shogo TakasukaGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
Shigehito AsanoJSR Corporation, 100 Kawajiri-cho, Yokkaichi, Mie 510-8552, Japan.
Ryo HashizumeJSR Corporation, 100 Kawajiri-cho, Yokkaichi, Mie 510-8552, Japan.
Miho HatanakaDepartment of Chemistry, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.ORCID https://orcid.org/0000-0001-9908-7860
Yu-Ya OhnishiJSR Corporation, 3-103-9 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-0821, Japan.
Takamitsu MatsubaraGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
Tsuyoshi AndoGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0001-5186-3867
Tetsunori SugawaraJSR Corporation, 100 Kawajiri-cho, Yokkaichi, Mie 510-8552, Japan.
Mikiya FujiiGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0002-3728-3097
Tomoyuki MiyaoGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0002-8769-2702
Hiroharu AjiroGraduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.ORCID https://orcid.org/0000-0003-4091-6956

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Copolymerization curves and reactivity-ratio estimates are traditionally derived from sparse offline data collected across many separate experiments. Here, we propose a data-efficient workflow for apparent finite-conversion reactivity-ratio estimation that combines gradient continuous-flow free-radical copolymerization, inline ATR-FTIR spectroscopy, and sparse linear regression to reconstruct dense concentration and composition trajectories from minimal offline quantitation. Using styrene/methyl methacrylate as a benchmark system, we performed a time-programmed zigzag sweep of the styrene/MMA feed ratio at constant total flow and acquired more than 700 inline ATR-FTIR spectra in a single ∼4 h run at each of 70, 80, and 90 °C. Only ten offline UHPLC anchor points per run were used to calibrate Lasso models for monomer concentration prediction, enabling high-density reconstruction of the reference monomer composition

Identifiers

PMID42577972
PMCPMC13456110

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.