Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection

doi:10.6023/A22030134

Acta Chimica Sinica ›› 2022, Vol. 80 ›› Issue (8): 1183-1202.DOI: 10.6023/A22030134 Previous Articles Next Articles

Review

金属有机框架在气体预富集、预分离及检测中的应用

闫续^a^,^b, 屈贺幂^a^,^b, 常烨^a^,^b, 段学欣^a^,^b^,^*()

a 天津大学精密仪器与光电子工程学院天津 300072
b 天津大学精密测试技术及仪器国家重点实验室天津 300072

投稿日期:2022-03-29 发布日期:2022-09-01
通讯作者: 段学欣

作者简介:

闫续, 博士在读, 2013年本科毕业于南开大学, 2016年硕士毕业于南开大学, 目前在天津大学精仪学院微机电系统(MEMS)实验室开展研究工作, 研究方向是微型气体传感器的开发以及金属有机框架材料在气体检测中的应用.

屈贺幂, 博士, 2004年本科毕业于湖南大学, 2007年硕士毕业于湖南大学, 2011年博士毕业于新加坡国立大学. 2014年加入天津大学MEMS实验室开展研究工作, 研究方向是用于气体传感器的敏感材料的开发以及气体检测设备的小型化.

常烨, 博士, 2013年本科毕业于天津大学, 2018年博士毕业于天津大学. 2018年加入天津大学MEMS实验室开展研究工作, 研究方向是薄膜体声波谐振器(FBAR)化学传感器和微/纳器件的化学功能化修饰.

段学欣, 天津大学教授, 博士生导师, 2001年本科毕业于南开大学, 2004年硕士毕业于南开大学, 2010年博士毕业于荷兰特温特大学. 2013年加入天津大学MEMS实验室开展研究工作, 研究方向是MEMS/NEMS器件的开发、微系统和微流体以及它们在化学、生物学、医学和环境科学中的应用.

Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection

Xu Yan^a^,^b, Hemi Qu^a^,^b, Ye Chang^a^,^b, Xuexin Duan^a^,^b()

a College of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072
b State Key Laboratory of Precision Measuring Technology & Instruments, Tianjin University, Tianjin 300072

Received:2022-03-29 Published:2022-09-01
Contact: Xuexin Duan

Gas separation and detection technology is closely related to people’s daily production and life. With the emergence of various detection equipment, it has made remarkable progress in recent decades. Gas sensing materials play an important role in the process of gas separation and detection. Compared with traditional gas sensing materials, metal-organic frameworks (MOFs), as a new type of porous materials, has ultra-large surface area, extremely high porosity, regular pore structure and adjustable pore size. These unique advantages make MOFs particularly suitable as promising candidates for the gas pre-concentration, pre-separation and detection, so as to realize the collection and enhancement of low concentration gas, the separation of complex gas mixtures, and the reliable detection of trace target gases. The detection ability and separation efficiency are greatly improved eventually. Therefore, MOFs are expected to surpass the traditional materials, and their application in gas separation and detection deserves the attention of both academia and industry.

Key words: gas detection, metal-organic frameworks, gas preconcentrator, gas chromatographic column, gas sensor

Cite this article

Xu Yan, Hemi Qu, Ye Chang, Xuexin Duan. Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection[J]. Acta Chimica Sinica, 2022, 80(8): 1183-1202.

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Fig. & Tab. 33

Figure 1. (a) Schematic of MOFs formation process, adapted from the literature [39]. (b) Comparison of BET surface areas of a part of MOFs in this paper and common porous materials

Figure 2. (a) Schematic of preconcentration, separation and detection process of targeted gas on gas preconcentrator, gas chromatographic column and gas sensor. (b) Schematic overview of this review

Figure 3. Schematic of physical adsorption of gas molecules on porous materials (1: external diffusion stage, 2: internal diffusion stage, 3: surface adsorption stage), adapted from the literature [43]

Figure 4. Schematic of selective gas adsorption by MOFs. Selectivity comes from (1) molecular sieving effect of MOFs materials, and (2) interactions between adsorbate and adsorbent surface; adapted from the literature [53]

Figure 5. (a) Photograph of conventional gas preconcentrator. (b) Photograph of micro gas preconcentrator; adapted from the literature [66]. (c) Comparison of signal peaks generated after the preconcentration of targeted gas by conventional gas preconcentrator and micro gas preconcentrator; adapted from the literature [6]

Figure 6. Comparison of GC chromatograms of thermal desorption after concentration of 642×10–3 cm3/m3 DMMP with preconcentration module filled with IRMOF1 and 651 cm3/m3 DMMP with empty preconcentration module; adapted from the literature [74]

Figure 7. (a) Schematic of sampling and desorption process on MOF-5 packed preconcentration tube. (b) Effect of relative humidity on the adsorption of formaldehyde by this preconcentration tube; Adapted from the literature [75]

Figure 8. (a) Photograph of the preconcentration tube packed with MOF-5. (b) GC chromatograms of formaldehyde obtained for ambient air with different sampling volumes; adapted from the literature [76]

Figure 9. (a) The repeatability of preconcentration effect of UiO-66 packed preconcentration tube on acetone and isopropanol. (b) The relationship between storage time of acetone and isopropanol in this tube and recovery of these targeted gas at room temperature; Adapted from the literature [77]

Figure 10. (a) Photograph of μPC with UiO-66 deposited in the flow channel. (b) Detector signal during thermal desorption of toluene from the μPC (Black curves, heating supplied by integrated heater; blue curves, heating supplied by convection oven; gray, reference measurement by μPC without UiO-66); adapted from the literature [80]

Figure 11. (a) Photograph of a integrated gas sensing microsystem contains a MOFs packed μPC (left) and two commercialized micro gas sensors (right); adapted from the literature [81]. (b) The relationship between preconcentration factor of μPC for benzene and sampling time; adapted from the literature [82]

Figure 12. (a) Photograph of μPC packed with MOFM. (b) FE-SEM image of the synthesized MOFM. (c) Comparison of preconcentration factor of BTEX in μPC with MOFM, RAD145 and Carbotrap B as adsorption materials. (d) The relationship between the pressure drop generated by the targeted vapor passing through these three μPCs and the gas flow rate; adapted from the literature [84]

MOFs种类	预富集器类型	被测气体	富集效果	参考文献
IRMOF1 (MOF-5)	自制气体预富集装置	甲基磷酸二甲酯	富集因子超过5000, 相同条件下Tenax TA的富集因子仅为2	[74]
MOF-5	传统气体预富集器	甲醛	富集效率分别是Tenax TA和石墨化炭黑的53 和73倍; 目标气体回收率为93%~107%	[75]
MOF-5	传统气体预富集器	甲醛	—	[76]
UiO-66	传统气体预富集器	丙酮、异丙醇	加标回收率可达89.1%~107.6%	[77]
UiO-66	微型气体预富集器	甲苯	最大富集因子可达13.7	[80]
HKUST-1	微型气体预富集器	苯	富集效果好于Tenax TA, 最大富集因子为7	[83]
MIL-53	微型气体预富集器	苯	富集效果好于Tenax TA	[83]
负载了MOF-5的泡沫镍	微型气体预富集器	苯系物	富集效果好于商品化富集材料RAD145和 Carbotrap B, 富集因子为144	[84]

Table 1. Application of MOFs in gas preconcentrators

Figure 13. (a) Photograph of conventional gas chromatographic column. (b) Photograph of micro gas chromatographic column; Adapted from the literature [19]. (c) Comparison of signal peaks of gas mixture separated by conventional gas chromatography column and micro gas chromatography column

Figure 14. (a) Schematic of MOF-508 structure. (b) Separation of an alkane mixture on a MOF-508 based column (1: 2-methylbutane, 2: n-pentane, 3: 2,2-dimethylbutane, 4: 2-methylpentane, 5: n-hexane); adapted from the literature [93]

Figure 15. (a) SEM images of ZIF-8 coated PLOT column. (b) Separation of a petrol sample on this column (1: 1,2-dimethylcyclopropane, 2: pent-2-ene, 3: pentane, 4: hex-3-ene, 5: hex-2-ene, 6: hexane, 7: hept-3-ene, 8: heptane, 9: oct-3-ene, 10: octane, 11: non-3-ene, 12: nonane, 13: decane, 14: undecane, 15: dodecane); adapted from the literature [95]

Figure 16. (a) Schematic of ZIF-90 coating on the capillary column modified by carboxyl groups. Separation of (b) linear alkanes, (c) benzene homologues, (d) aldehydes, (e) n-alcohols, (f) 2-alcohols, (g) ketones on this column; adapted from the literature [98]

Figure 17. Separation of (a) isoleucine derivative, (b) citronellal, (c) 1-phenyl-1,2-ethandiol, (d) leucine derivative, (e) phenyl-succinic acid, (f) 1-phenyl-ethanol, (g) valine derivative, (h) alanine derivative, (i) proline derivative, (j) camphor and (k) 2-methyl-1-butanol derivative on chiral MOFs ({Cu(sala)}n) based open tubular column; adapted from the literature [102]

Figure 18. (a) Photograph of a μGC×μGC contains a HKUST-1 coated μGC column and a ZIF-8 coated μGC column. (b) 3D chromatogram showing the separation of light alkanes with 1~4 carbon on this μGC×μGC; adapted from the literature [103]

Figure 19. (a) Photograph of a ZIF-8 coated μGC column. (b) The separation of light alkane mixtures with 1~4 carbon on this μGC; adapted from the literature [104]

MOFs种类	预富集器类型	分析物种类	分离效果	参考文献
MOF-508	气相色谱填充柱	烷烃混合物	实现了含有5种直链、支链烷烃的混合物的成功分离	[93]
ZIF-8	PLOT气相色谱柱	烷烃、烯烃混合物	实现了含有15种直链、支链烷烃、烯烃的汽油样品的成功分离	[95]
ZIF-90	PLOT气相色谱柱	烷烃、苯系物、醛类、醇类和酮类化合物	分别实现了多5种直链烷烃、苯系物、醛类化合物、正醇类化合物、 2位是羟基的醇类化合物,以及酮类化合物的成功分离	[99]
[{Cu(sala)}_n]	气相色谱开管柱	外消旋体	分别实现了包含氨基酸衍生物、香茅醛、醇类化合物和樟脑在内的11 种外消旋体的成功分离	[102]
ZIF-8和HKUST-1	二维微型气相色谱柱	烷烃	实现了5种直链、支链烷烃混合物的成功分离, 并且可以区分出正丁烷及其同分异构体	[103]
ZIF-8	微型气相色谱柱	烷烃	实现了4种直链烷烃混合物的成功分离	[104]

Table 2. Application of MOFs in gas chromatographic columns

Figure 20. (a) Photograph of ZIF-8 films with various thicknesses grown on silicon substrate. (b) The relationship between the shift of characteristic peak in transmission spectrum and the propane concentrations. (c) The relationship between the shift of characteristic peak in transmission spectrum and the ethanol concentrations; adapted from the literature [114]

Figure 21. (a) Schematic of a MOFs based FO-SPR probe. (b) The sensitivity curves of ZIF-8 and ZIF-93 modified sensors to methanol and n-butanol. (c) Selectivity of these sensors for methanol and n-butanol; adapted from the literature [119]

Figure 22. (a) Schematic of BTEX vapors detections by a NH2-MIL- 88B/P(St-AA) based photonic crystal sensor. (b) The relationship between the wavelength changes of this sensor and the concentrations of BTEX vapors; adapted from the literature [122]

Figure 23. (a) Schematic of the preparation process of resistance gas sensors with ZnO@ZIF-CoZn as sensitive materials. (b) The relationship between the responses of these sensors to acetone and their operating temperature. (c) The response-recovery curves of these sensors to 100 cm3/m3 acetone at 260 ℃; adapted from the literature [124]

Figure 24. (a) Schematic of preparation process of MIL-101(Cr) and PEDOT composites. (b) The real-time response of resistance gas sensor based on this composite material to nitrogen dioxide. (c) The relationship between the response of this sensor and nitrogen dioxide concentrations; adapted from the literature [125]

Figure 25. (a) Schematic of Cu3(HITP)2 structure. (b) Schematic of resistance gas sensor with Cu3(HITP)2 as sensitive materials and the experimental setup used in this work. (c) The real-time response of this sensor to ammonia. (d) The relationship between the sensor response and ammonia concentrations; adapted from the literature [129]

Figure 26. (a) SEM image of the HKUST-1 modified microcantilever gas sensor. (b) The real-time response of this sensor to p-xylene. (c) The selectivity of this sensor for 12 vapors with a concentration of 50 cm3/m3; adapted from the literature [132]

Figure 27. (a) Schematic of ZIF-8 structure and it applied as sensitive materials to surface acoustic wave (SAW) and quartz crystal microbalance (QCM) transducers. (b) The real-time phase of a 3-cycle ZIF-8 film coated SAW sensor measured in wired mode upon exposure to 4 kinds of gases; adapted from the literature [136]

Figure 28. (a) Schematic of the film bulk acoustic resonator (FBAR). (b) The relationship between the responses of PDMS@HKUST-1 modified FBAR sensor and the concentrations of 6 VOCs and water vapor. The responses of (c) HKUST-1 modified sensor and (d) PDMS@HKUST-1 modified sensor to ethanol before and after water treatment; adapted from the literature [143]

Figure 29. (a) Schematic of Cu3(HHTP)2, Cu3(HITP)2 and Ni3(HITP)2 structures. (b) Distinguish result of 5 types of VOCs by the three MOFs modified sensor array combined with principal component analysis (PCA) algorithm; Adapted from the literature [145]

Figure 30. (a) Schematic of an e-nose composed of 6 QCM sensors modified by 3 chiral MOFs and 3 achiral MOFs membranes. (b) Recognition results of 5 five pairs of chiral odor molecules by this e-nose combined with k-nearest-neighbor (kNN) algorithm; adapted from the literature [147]

MOFs种类	传感器类型	被测气体	测试结果	参考文献
ZIF-8	Fabry-Pérot干涉仪(光学)	乙醇、丙烷	乙醇和丙烷浓度分别为40%和100%时传感器的响应最大	[114]
ZIF-8、ZIF-93	FO-SPR传感器(光学)	甲醇、正丁醇	成功检测到浓度为cm³/m³的被测气体, 其中ZIF-8、ZIF-93修饰的传感器分别适用于极性小和极性大的气体	[120]
NH₂-MIL-88B和 P(St-AA)复合材料	多层光子晶体传感器(光学)	苯系物	对苯、甲苯、乙苯和二甲苯的检测限分别为1062.87、211.50、 88.62和42.60 cm³/m³	[122]
ZIF-8和GQDs 复合材料	三维光子晶体传感器(光学)	苯	对苯的检测限为1 cm³/m³	[123]
ZIF-CoZn和ZnO 复合材料	电阻式传感器 (电学)	丙酮	ZIF-CoZn修饰后的传感器的响应值增大了约20倍, 检测限降低了2个数量级	[124]
MIL-101(Cr)和 PEDOT复合材料	电阻式传感器 (电学)	二氧化氮	对二氧化氮的检测限为60×10^–3 cm³/m³, 线性检测范围为0~10 cm³/m³	[125]
Cu₃(HITP)₂	电阻式传感器 (电学)	氨气	对氨气的检测限为0.5 cm³/m³, 线性检测范围为0.5~10 cm³/m³	[129]
HKUST-1	微型悬臂梁传感器(质量型)	邻二甲苯	对邻二甲苯的检测限为400×10^–3 cm³/m³	[132]
UiO-66	微型悬臂梁传感器(质量型)	甲基磷酸二甲酯	对甲基磷酸二甲酯的检测限为5×10^–3 cm³/m³	[133]
ZIF-8	SAW传感器 (质量型)	二氧化碳	相比于甲烷、一氧化碳和氢气, ZIF-8修饰后的传感器对二氧化碳的响应更大, 并且该传感器可以在无线工作模式下实现对二氧化碳的检测	[136]
HKUST-1和PDMS 复合材料	FBAR传感器 (质量型)	水蒸气和6种常见的VOCs	对水蒸气和甲醇、乙醇、正丙醇、环己烷、正己烷和正庚烷等6种 VOCs的检测限为几个cm³/m³, 线性检测范围为(20~100) cm³/m³	[143]
3种导电的二维MOFs	电阻式传感器阵列(e-nose)	16种VOCs	与主PCA算法结合, 可以成功地将16种VOCs归为醇类、酮/醚类、胺类、芳香烃类和脂肪族类等5类; 与LDA算法结合可以实现92%的分类准确率	[145]
3种非手性MOFs和 3种手性MOFs	QCM传感器阵列(e-nose)	5对手性气味分子	成功地实现对5对手性气味分子的选择性检测和鉴别, 平均准确率为96%	[147]

MOFs种类	传感器类型	被测气体	测试结果	参考文献
ZIF-8	Fabry-Pérot干涉仪(光学)	乙醇、丙烷	乙醇和丙烷浓度分别为40%和100%时传感器的响应最大	[114]
ZIF-8、ZIF-93	FO-SPR传感器(光学)	甲醇、正丁醇	成功检测到浓度为cm³/m³的被测气体, 其中ZIF-8、ZIF-93修饰的传感器分别适用于极性小和极性大的气体	[120]
NH₂-MIL-88B和 P(St-AA)复合材料	多层光子晶体传感器(光学)	苯系物	对苯、甲苯、乙苯和二甲苯的检测限分别为1062.87、211.50、 88.62和42.60 cm³/m³	[122]
ZIF-8和GQDs 复合材料	三维光子晶体传感器(光学)	苯	对苯的检测限为1 cm³/m³	[123]
ZIF-CoZn和ZnO 复合材料	电阻式传感器 (电学)	丙酮	ZIF-CoZn修饰后的传感器的响应值增大了约20倍, 检测限降低了2个数量级	[124]
MIL-101(Cr)和 PEDOT复合材料	电阻式传感器 (电学)	二氧化氮	对二氧化氮的检测限为60×10^–3 cm³/m³, 线性检测范围为0~10 cm³/m³	[125]
Cu₃(HITP)₂	电阻式传感器 (电学)	氨气	对氨气的检测限为0.5 cm³/m³, 线性检测范围为0.5~10 cm³/m³	[129]
HKUST-1	微型悬臂梁传感器(质量型)	邻二甲苯	对邻二甲苯的检测限为400×10^–3 cm³/m³	[132]
UiO-66	微型悬臂梁传感器(质量型)	甲基磷酸二甲酯	对甲基磷酸二甲酯的检测限为5×10^–3 cm³/m³	[133]
ZIF-8	SAW传感器 (质量型)	二氧化碳	相比于甲烷、一氧化碳和氢气, ZIF-8修饰后的传感器对二氧化碳的响应更大, 并且该传感器可以在无线工作模式下实现对二氧化碳的检测	[136]
HKUST-1和PDMS 复合材料	FBAR传感器 (质量型)	水蒸气和6种常见的VOCs	对水蒸气和甲醇、乙醇、正丙醇、环己烷、正己烷和正庚烷等6种 VOCs的检测限为几个cm³/m³, 线性检测范围为(20~100) cm³/m³	[143]
3种导电的二维MOFs	电阻式传感器阵列(e-nose)	16种VOCs	与主PCA算法结合, 可以成功地将16种VOCs归为醇类、酮/醚类、胺类、芳香烃类和脂肪族类等5类; 与LDA算法结合可以实现92%的分类准确率	[145]
3种非手性MOFs和 3种手性MOFs	QCM传感器阵列(e-nose)	5对手性气味分子	成功地实现对5对手性气味分子的选择性检测和鉴别, 平均准确率为96%	[147]

Table 3. Application of MOFs in gas sensors

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金属有机框架在气体预富集、预分离及检测中的应用

Application of Metal-Organic Frameworks in Gas Pre-concentration, Pre-separation and Detection

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