Evidence map›Paper›PMID 37688115›Full record

ArticleSensors (Basel, Switzerland)2023

Smart Temperature Sensor Design and High-Density Water Temperature Monitoring in Estuarine and Coastal Areas.

Bozhi Wang, Huayang Cai, Qi Jia, Huimin Pan, Bo Li, Linxi Fu

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 1 institution in 1 country.

Bozhi WangInstitute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China.
Huayang CaiInstitute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China.ORCID 0000-0001-7327-4879
Qi JiaSiSensor Technology Company, Zhuhai 519082, China.
Huimin PanInstitute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China.
Bo LiInstitute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China.
Linxi FuInstitute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China.
Sun Yat-sen University · CN

Funding

National Natural Science Foundation of China Grant No. 52279080 and 51979296
6 · The paper itself

Abstract

Acquiring in situ water temperature data is an indispensable and important component for analyzing thermal dynamics in estuarine and coastal areas. However, the long-term and high-density monitoring of water temperature is costly and technically challenging. In this paper, we present the design, calibration, and application of the smart temperature sensor TS-V1, a low-power yet low-cost temperature sensor for monitoring the spatial-temporal variations of surface water temperatures and air temperatures in estuarine and coastal areas. The temperature output of the TS-V1 sensor was calibrated against the Fluke-1551A sensor developed in the United States and the CTD-Diver sensor developed in the Netherlands. The results show that the accuracy of the TS-V1 sensor is 0.08 °C, while sensitivity tests suggest that the TS-V1 sensor (comprising a titanium alloy shell with a thermal conductivity of 7.6 W/(m °C)) is approximately 0.31~0.54 s/°C slower than the CTD-Diver sensor (zirconia shell with thermal conductivity of 3 W/(m °C)) in measuring water temperatures but 6.92~10.12 s/°C faster than the CTD-Diver sensor in measuring air temperatures. In addition, the price of the proposed TS-V1 sensor is only approximately 1 and 0.3 times as much as the established commercial sensors, respectively. The TS-V1 sensor was used to collect surface water temperature and air temperature in the western part of the Pearl River Estuary from July 2022 to September 2022. These data wells captured water and air temperature changes, frequency distributions, and temperature characteristics. Our sensor is, thus, particularly useful for the study of thermal dynamics in estuarine and coastal areas.

Indexed as

high-accuracy sensorshigh-density monitoringhigh-sensitivity sensorslong-termlow-cost sensors

Identifiers

PMID37688115
PMCPMC10490809
OpenAlexW4386423279

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.