Evidence map›Paper›PMID 37514566›Full record

ArticleSensors (Basel, Switzerland)2023

Online Monitoring of Seawater Carbon Dioxide Based on an Infrared Rear Beam Splitter.

Luyin Liu, Ruzhang Liu, Guochao Ma, Shanshan Feng, Yuanhui Mu, Dexi Meng, Shuying Wang, Enlin Cai

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

8 authors at 1 institution in 1 country.

Luyin LiuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.ORCID 0000-0002-8345-7369
Ruzhang LiuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.ORCID 0000-0003-4527-8607
Guochao MaThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Shanshan FengThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Yuanhui MuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Dexi MengThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Shuying WangThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Enlin CaiThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ocean is one of the most extensive ecosystems on Earth and can absorb large amounts of carbon dioxide. Changes in seawater carbon dioxide concentrations are one of the most important factors affecting marine ecosystems. Excess carbon dioxide can lead to ocean acidification, threatening the stability of marine ecosystems and species diversity. Dissolved carbon dioxide detection in seawater has great scientific significance. Conducting online monitoring of seawater carbon dioxide can help to understand the health status of marine ecosystems and to protect marine ecosystems. Current seawater detection equipment is large and costly. This study designed a low-cost infrared carbon dioxide detection system based on molecular theory. Using the HITRAN database, the absorption spectra and coefficients of carbon dioxide molecules under different conditions were calculated and derived, and a wavelength of 2361 cm

Indexed as

HITRANinfrared detectionmarine ecologyseawater carbon dioxide

Identifiers

PMID37514566
PMCPMC10386597
OpenAlexW4383912343

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.