生物质炭材料在气体传感检测中的应用进展

邱璐; 陈享享; 刘天豪; 柳方正; 欧阳云飞; 黄世毅; 张朝阳; 罗潇涵; 邱小玲

生物质炭材料在气体传感检测中的应用进展

邱璐^{1, 2},
陈享享^{1, 2, ,},
刘天豪^{1, 2},
柳方正^{1, 2},
欧阳云飞^{1, 2},
黄世毅^{1, 2},
张朝阳^{1, 2},
罗潇涵^{1, 2},
邱小玲^{1, 2}

1.
福州大学紫金地质与矿业学院, 福州 350108
2.
福州大学能源金属高值化省重点实验室, 福州 350108

基金项目: 国家自然科学基金项目(No.52004069)

详细信息

通讯作者:
陈享享，博士，副研究员，硕士生导师，研究方向为矿业安全材料和气体传感器　E-mail: chen@fzu.edu.cn

中图分类号: TP212;TB332
计量
- 文章访问数: 46
- HTML全文浏览量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-28
- 修回日期: 2024-07-16
- 录用日期: 2024-07-20
- 网络出版日期: 2024-08-02

Advances in the application of biomass charcoal materials for gas sensing detection

QIU Lu^{1, 2},
CHEN Xiangxiang^{1, 2
, ,},
LIU Tianhao^{1, 2},
LIU Fangzheng^{1, 2},
OUYANG Yunfei^{1, 2},
HUANG Shiyi^{1, 2},
ZHANG Zhaoyang^{1, 2},
LUO Xiaohan^{1, 2},
QIU Xiaoling^{1, 2}

1.
School of Zijin Geology and Mining, Fuzhou University, Fuzhou, 350000, China
2.
Provincial Key Laboratory of High-Value Energy Metals, Fuzhou University, Fuzhou 350000, China

Funds: National Natural Science Foundation of China (No.52004069)

摘要

摘要: 随着全球能源需求的增长和环境问题的加剧，开发新型高性能气体传感器变得尤为迫切。生物质炭材料是由生物质原料经过预碳化和活化处理获得，具有独特的孔隙结构、大的比表面积、丰富的表面活性官能团和活性位点，在气体传感检测领域具有巨大的应用潜力。本文按照生物质炭的主要来源对生物质进行了分类(植物基、动物基和微生物基)以及四种生物质炭材料的常见制备方法(水热炭化法、活化法、模板法和微波热解法)。本文重点讨论了生物质炭材料在半导体型气体传感器和非金属氧化物主导型气体传感器的最新研究进展，包括作为气敏材料在检测各类气体方面的应用。最后，分析了生物质炭材料基气体传感器目前需要解决的问题，为拓宽该类传感器的实际应用提出了研发的思路。
- 生物质 /
- 炭材料 /
- 传感器 /
- 气敏 /
- 半导体 /
- 非金属氧化物
Abstract: The development of new high-performance gas sensors has become particularly urgent with the growth of global energy demand and the aggravation of environmental problems. Biomass char materials are obtained from biomass raw materials through pre-carbonization and activation treatment, which have unique pore structure, large specific surface area, abundant surface active functional groups and active sites, and have great potential for application in the field of gas sensing and detection. In this paper, biomass is classified according to the main sources of biomass char (plant-based, animal-based, and microbial-based) as well as four common preparation methods of biomass char materials (hydrothermal carbonization, activation, templating, and microwave pyrolysis). The paper focuses on the recent research progress of biomass char materials in semiconductor-based gas sensors and non-metallic oxide dominated gas sensors, including their applications as gas-sensitive materials for the detection of various types of gases. Finally, the current problems that need to be solved for biomass charcoal material-based gas sensors are analyzed, and ideas for research and development are proposed to broaden the practical applications of such sensors.
- biomass /
- carbon material /
- sensor /
- gas sensing /
- semiconductor /
- non-metallic oxide

HTML全文

图 1 不同水热温度制备生物炭的扫描电子显微镜图：(a) 未处理污泥；(b) 130℃；(c) 180℃；(d) 220℃

Figure 1. SEM images of biochar prepared at different hydrothermal temperatures: (a) untreated sludge; (b) 130℃; (c) 180℃; (d) 220℃

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图 2 CSB800-CO₂-30 (a、b、c)和CSB800-H₂O-60(d、e、f)样品图及表面形态

Figure 2. Sample diagrams and surface morphology of CSB800-CO₂-30 (a、b、c) and CSB800-H₂O-60 (d、e、f)

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图 3 (a) KOH-K；(b) KOH-KB；(c) H₃PO₄-K；(d) H₃PO₄-KB；(e) MnO₂/KOH-KB；(f) MnO₂/H₃PO₄-KB活性炭的扫描电子显微镜图

Figure 3. SEM microstructures of the activated carbons: (a) KOH-K; (b) KOH-KB; (c) H₃PO₄-K; (d) H₃PO₄-KB; (e) MnO₂/KOH-KB; (f) MnO₂/ H₃PO₄-KB

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图 4 HPC样品在2000×和20000×放大倍率(内插图)下的扫描电子显微镜图

Figure 4. SEM images of HPC samples at 2000× and 20000× magnifications

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图 5 (a) CSs, (c) CSF, (e) CSI的扫描电子显微镜图片；(b) CSs, (d) CSF的透射电子显微镜图片；(f) CSI, (g) CSF和(h) CSI的高分辨率透射电子显微镜图片

Figure 5. SEM images of (a) CSs, (c) CSF, (e) CSI; TEM images of (b) CSs and (d) CSF; high-resolution TEM images of (f) CSI, (g) CSF and (h) CSI

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图 6 (a) Ni@NSiC的X射线衍射图；(b) NSiC和(c) Ni@NSiC-900的扫描电子显微镜图；(d)和(e) Ni@NSiC-900的透射电子显微镜图；(f) Ni@NSiC-900的高分辨率的透射电子显微镜图(插图为选区电子衍射图)；(g-k)Ni@NSiC-900的元素图谱结果

Figure 6. (a) XRD pattern of Ni@NSiC; (b) and (c) SEM images of Ni@NSiC-900; (d) and (e) TEM images of Ni@NSiC-900; (f) HR-TEM image of Ni@NSiC-900 (inset figure SAED pattern); (g-k) elemental mapping results of Ni@NSiC-900

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图 7 催化型、电化学型和MOS型气体传感器的传感范围比较^[59]

Figure 7. Sensing range comparison of catalytic-type, electrochemical-type, and MOS-type gas sensors^[59]

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图 8 (a) 对丝胶蛋白包覆的ZNRs进行形貌观察：(a)顶部、横截面和更高放大倍率的场发射扫描电子显微镜图；(b) 高分辨率的透射电子显微镜图

Figure 8. Morphological observation of obtained sericin capped ZNRs:(a) FESEM images of the top, cross-section and higher magnification;(b) HRTEM image

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图 9 (a) CoBC-700传感器在RT(相对湿度25%)条件下对NO₂的动态响应-恢复曲线和响应时间；(b) CoBC-600、CoBC-700和CoBC-800传感器对100-0.01×10⁻⁶ NO₂的响应和响应时间(其中直方图为响应值，线性图为响应时间)；(c) CoBC-700传感器对0.01-100×10⁻⁶ NO₂的校准曲线；(d) CoBC-700传感器连续暴露于30×10⁻⁶ NO₂(7个周期)的重现性；(e) CoBC-700传感器对各种气体响应的选择性测试；(f) CoBC-700传感器在35天内对100×10⁻⁶ NO₂的稳定性测试

Figure 9. (a) Dynamic response-recovery curve and response time of the CoBC-700 sensor to NO₂ at the RT (RH 25%); (b) Response and response time of CoBC-600, CoBC-700 and CoBC-800 sensor from 100 to 0.01 ×10⁻⁶ NO₂ (Among them, the histogram is the response value, and the linear graph is the response time); (c) The calibration curve of CoBC-700 sensor to 0.01-100×10⁻⁶ NO₂; (d) The reproducibility of CoBC-700 sensor continuously exposed to 30×10⁻⁶ NO₂ (7 cycles); (e) Selective testing of the response of CoBC-700 sensor to various gases；(f) The stability test of CoBC-700 sensor to 100×10⁻⁶ NO₂ within 35 days

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图 10 (a) In₂O₃-600传感器在92℃下对不同浓度NO₂气体的响应-恢复曲线；(b) In₂O₃-600传感器的响应与不同浓度NO₂气体之间的关系；In₂O₃-600传感器对100×10⁻⁹ (c)和1×10⁻⁶ (d) NO₂气体的动态响应-恢复曲线^[87]

Figure 10. (a) Response–recovery curves of In₂O₃-600 sensor to different concentrations of NO₂ gas at 92 °C；(b) The relationship between responses of In₂O₃-600 sensor and different concentrations of NO₂ gas; Dynamic response-recovery curves of In₂O₃-600 sensor to 100×10⁻⁹ (c) and 1 ×10⁻⁶ (d) NO₂ gas^[87]

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表 1 不同原料生物质炭理化性质比较(平均值±同原料置信区间)^[30]

Table 1. Physical and chemical properties of biochars derived from different feedstocks (mean ± 95% confidence interval)^[30]

Raw material	pH	Total organic carbon/%	Surface area/(m²·g⁻¹)	Ash/%	Total N/%	Total P/%	Total K/%
Wood	8.2±0.2	72.6±1.3	164.4±24.1	7.84±1.1	0.6±0.1	0.4±0.1	0.7±0.2
Crop residue	9.5±0.2	61.1±1.6	109.2±1.7	23.6±1.7	1.3±0.1	0.7±0.1	3.3±0.5
Grass	8.7±0.3	63.9±1.8	63.4±2.3	17.9±2.3	1.2±0.1	0.4±0.1	1.7±0.4
Manure	9.4±0.2	45.6±2.7	36.6±3.1	38.82±3.1	2.5±0.2	2.4±0.4	3.2±0.4
Sludge	8.7±0.5	25.1±2.7	28.2±4.3	63.3±4.3	2.5±0.3	3.0±0.7	1.1±0.3
Sec-biowaste	8.8±0.3	59.2±3.0	71.3±4.1	23.1±4.1	2.7±0.3	1.2±0.5	1.2±0.3

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