Article(id=1203753463017615967, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2308910, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1699804800000, receivedDateStr=2023-11-13, revisedDate=1729180800000, revisedDateStr=2024-10-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1764926790241, onlineDateStr=2025-12-05, pubDate=1737129600000, pubDateStr=2025-01-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764926790241, onlineIssueDateStr=2025-12-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764926790241, creator=13701087609, updateTime=1764926790241, updator=13701087609, issue=Issue{id=1203753457208504777, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='2', pageStart='439', pageEnd='878', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764926788856, creator=13701087609, updateTime=1764928745558, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1203761664261858014, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1203761664261858015, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1203753457208504777, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=862, endPage=870, ext={EN=ArticleExt(id=1203753464003277497, articleId=1203753463017615967, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Optimization of Electrospinning Process for Preparation of BiFeO3 by Response Surface Methodology, columnId=1156262729993277777, journalTitle=Science Technology and Engineering, columnName=Papers·Environmental and Safe Science, runingTitle=null, highlight=null, articleAbstract=

Bismuth ferrite has become an effective semiconductor photocatalyst for the degradation of various wastewaters due to its narrow band gap, high chemical stability, and good visible light response. Pure phase BiFeO3 nanofibers were prepared by electrospinning method. The optimal degradation conditions for Congo Red were obtained through single factor experiments such as calcination temperature, PVP (polyvinyl pyrrolidone)concentration, collection distance, spinning voltage, and pushing speed. Four factors that significantly affect photocatalytic efficiency were selected for response surface analysis experiments with four factors and three levels. After optimization, the optimal PVP concentration was 12.17 wt%, collection distance was 14.07 cm, and spinning voltage was 12.03 kV The pushing speed is 0.74 μm/s, and under this condition, the efficiency of BiFeO3 photocatalytic degradation of Congo red can reach 90.43%. The phase analysis and morphology characterization of bismuth ferrite nanofibers were carried out using X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy. The results show that the pure phase BFO nanofibers prepared by electrospinning has a rough surface and obvious particle sensation, presenting a one-dimensional rod-shaped structure with a size of about 300 nm. This nanorod-shaped structure has a larger specific surface area and more active sites, Can improve the photocatalytic degradation efficiency of BFO.

, correspAuthors=Dai-hong KUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yan-mei LI, Jia-rui ZHANG, Dai-hong KUANG, Rousuli AWABAIKELI), CN=ArticleExt(id=1203753469392957752, articleId=1203753463017615967, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=响应面法优化静电纺丝法制备BiFeO3的工艺研究, columnId=1156262730140078420, journalTitle=科学技术与工程, columnName=论文·环境科学、安全科学, runingTitle=null, highlight=null, articleAbstract=

铁酸铋其较窄的带隙、较高的化学稳定性、较好的可见光响应能力等优势使之成为降解多种废水的有效半导体光催化剂。采用静电纺丝法制备纯相BiFeO3纳米纤维,通过煅烧温度、聚乙烯吡咯烷酮(polyvinyl pyrrolidone,PVP)浓度、收集距离、纺丝电压、推料速度等单因素试验得到对刚果红最佳降解条件后,选择对光催化效率有显著影响的4个因素,进行四因素三水平的响应面分析试验,经过优化得到最优PVP浓度为12.17 wt%、收集距离为14.07 cm、纺丝电压为12.03 kV、推料速度为0.74 μm/s,在此条件下BiFeO3光催化降解刚果红的效率可达90.43%。利用X射线衍射、扫描电子显微镜、拉曼光谱、傅里叶变换红外光谱对铁酸铋纳米纤维进行物相分析和形貌表征,结果表明静电纺丝法制备的纯相BiFeO3纳米纤维表面粗糙有明显的颗粒感,呈一维棒管状结构,尺寸约为300 nm,这种纳米棒管结构具有更大的比表面积及更多的活性位点,能够提高BFO光催化降解效率。

, correspAuthors=匡代洪, authorNote=null, correspAuthorsNote=
* 匡代洪(1977—),男,汉族,湖南衡阳人,博士,副教授,硕士研究生导师。研究方向:环境功能材料的制备及性能。E-mail:
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李艳美(1999—),女,汉族,四川邻水人,硕士研究生。研究方向:BiFeO3纳米材料光催化降解有机污染物。E-mail:

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李艳美(1999—),女,汉族,四川邻水人,硕士研究生。研究方向:BiFeO3纳米材料光催化降解有机污染物。E-mail:

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李艳美(1999—),女,汉族,四川邻水人,硕士研究生。研究方向:BiFeO3纳米材料光催化降解有机污染物。E-mail:

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Level and code of variables for Box-Behnken design

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水平 因素
A/wt% B/cm C/kV D/(μm·s-1)
-1 11 13 9 0.5
0 12 14 11 1
1 13 15 13 1.5
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Box-Behnken 设计试验因素水平及编码

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水平 因素
A/wt% B/cm C/kV D/(μm·s-1)
-1 11 13 9 0.5
0 12 14 11 1
1 13 15 13 1.5
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Box-Benhnken design and results

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试验号 A/wt% B/cm C/kV D/(μm·s-1) Y/%
1 12 14 9 0.5 71.14
2 12 14 13 1.5 71.85
3 11 14 9 1 65.78
4 12 15 11 1.5 81.04
5 12 15 13 1 81.02
6 13 14 11 0.5 77.07
7 13 15 11 1 83.81
8 11 15 11 1 74.85
9 13 14 11 1.5 72.43
10 12 15 9 1 77.46
11 12 13 9 1 67.56
12 11 14 11 0.5 77.46
13 12 14 11 1 88.6
14 12 13 13 1 83.96
15 13 13 11 1 76.1
16 13 14 9 1 66.53
17 12 14 11 1 87.69
18 11 13 11 1 70.06
19 12 14 9 1.5 69.71
21 12 13 11 0.5 80.76
22 13 14 13 1 81.49
23 12 13 11 1.5 70.77
24 12 14 11 1 86.23
25 11 14 11 1.5 70.27
26 11 14 13 1 72.43
27 12 15 11 0.5 82.47
28 12 14 11 1 89.95
29 12 14 11 1 87.58
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响应面试验设计及结果

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试验号 A/wt% B/cm C/kV D/(μm·s-1) Y/%
1 12 14 9 0.5 71.14
2 12 14 13 1.5 71.85
3 11 14 9 1 65.78
4 12 15 11 1.5 81.04
5 12 15 13 1 81.02
6 13 14 11 0.5 77.07
7 13 15 11 1 83.81
8 11 15 11 1 74.85
9 13 14 11 1.5 72.43
10 12 15 9 1 77.46
11 12 13 9 1 67.56
12 11 14 11 0.5 77.46
13 12 14 11 1 88.6
14 12 13 13 1 83.96
15 13 13 11 1 76.1
16 13 14 9 1 66.53
17 12 14 11 1 87.69
18 11 13 11 1 70.06
19 12 14 9 1.5 69.71
21 12 13 11 0.5 80.76
22 13 14 13 1 81.49
23 12 13 11 1.5 70.77
24 12 14 11 1 86.23
25 11 14 11 1.5 70.27
26 11 14 13 1 72.43
27 12 15 11 0.5 82.47
28 12 14 11 1 89.95
29 12 14 11 1 87.58
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Variance analysis table of regression equation

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来源 平方和 自由度 均方 F P 显著性
模型 1 513.79 14 108.13 32.39 <0.000 1 **
A 58.87 1 58.87 17.64 0.000 9 **
B 82.37 1 82.37 24.67 0.000 2 **
C 320.23 1 320.23 95.92 <0.000 1 **
D 148.76 1 148.76 44.56 <0.000 1 **
AB 2.13 1 2.13 0.64 0.437 6
AC 17.26 1 17.26 5.17 0.039 2 *
AD 1.63 1 1.63 0.49 0.496 7
BC 41.22 1 41.22 12.35 0.003 4 **
BD 18.32 1 18.32 5.49 0.034 5 *
CD 65.12 1 65.12 19.51 0.000 6 **
A2 481.23 1 481.23 144.15 <0.000 1 **
B2 75.68 1 75.68 22.67 0.000 3 **
C2 350.85 1 350.85 105.1 <0.000 1 **
D2 185.63 1 185.63 55.61 <0.000 1 **
残差 46.74 14 3.34
失拟项 39.17 10 3.92 2.07 0.251 8 不显著
纯误差 7.57 4 1.89
总误差 1 560.53 28
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回归方程的方差分析表

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来源 平方和 自由度 均方 F P 显著性
模型 1 513.79 14 108.13 32.39 <0.000 1 **
A 58.87 1 58.87 17.64 0.000 9 **
B 82.37 1 82.37 24.67 0.000 2 **
C 320.23 1 320.23 95.92 <0.000 1 **
D 148.76 1 148.76 44.56 <0.000 1 **
AB 2.13 1 2.13 0.64 0.437 6
AC 17.26 1 17.26 5.17 0.039 2 *
AD 1.63 1 1.63 0.49 0.496 7
BC 41.22 1 41.22 12.35 0.003 4 **
BD 18.32 1 18.32 5.49 0.034 5 *
CD 65.12 1 65.12 19.51 0.000 6 **
A2 481.23 1 481.23 144.15 <0.000 1 **
B2 75.68 1 75.68 22.67 0.000 3 **
C2 350.85 1 350.85 105.1 <0.000 1 **
D2 185.63 1 185.63 55.61 <0.000 1 **
残差 46.74 14 3.34
失拟项 39.17 10 3.92 2.07 0.251 8 不显著
纯误差 7.57 4 1.89
总误差 1 560.53 28
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响应面法优化静电纺丝法制备BiFeO3的工艺研究
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李艳美 1 , 张佳睿 1 , 匡代洪 1, 2, * , 阿瓦拜克力·肉苏里 2
科学技术与工程 | 论文·环境科学、安全科学 2025,25(2): 862-870
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科学技术与工程 | 论文·环境科学、安全科学 2025, 25(2): 862-870
响应面法优化静电纺丝法制备BiFeO3的工艺研究
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李艳美1 , 张佳睿1, 匡代洪1, 2, * , 阿瓦拜克力·肉苏里2
作者信息
  • 1 新疆农业大学资源与环境学院, 乌鲁木齐 830052
  • 2 新疆农业大学数理学院, 乌鲁木齐 830052
  • 李艳美(1999—),女,汉族,四川邻水人,硕士研究生。研究方向:BiFeO3纳米材料光催化降解有机污染物。E-mail:

通讯作者:

* 匡代洪(1977—),男,汉族,湖南衡阳人,博士,副教授,硕士研究生导师。研究方向:环境功能材料的制备及性能。E-mail:
Optimization of Electrospinning Process for Preparation of BiFeO3 by Response Surface Methodology
Yan-mei LI1 , Jia-rui ZHANG1, Dai-hong KUANG1, 2, * , Rousuli AWABAIKELI2
Affiliations
  • 1 College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China
  • 2 College of Mathematics and Science, Xinjiang Agricultural University, Urumqi 830052, China
出版时间: 2025-01-18 doi: 10.12404/j.issn.1671-1815.2308910
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铁酸铋其较窄的带隙、较高的化学稳定性、较好的可见光响应能力等优势使之成为降解多种废水的有效半导体光催化剂。采用静电纺丝法制备纯相BiFeO3纳米纤维,通过煅烧温度、聚乙烯吡咯烷酮(polyvinyl pyrrolidone,PVP)浓度、收集距离、纺丝电压、推料速度等单因素试验得到对刚果红最佳降解条件后,选择对光催化效率有显著影响的4个因素,进行四因素三水平的响应面分析试验,经过优化得到最优PVP浓度为12.17 wt%、收集距离为14.07 cm、纺丝电压为12.03 kV、推料速度为0.74 μm/s,在此条件下BiFeO3光催化降解刚果红的效率可达90.43%。利用X射线衍射、扫描电子显微镜、拉曼光谱、傅里叶变换红外光谱对铁酸铋纳米纤维进行物相分析和形貌表征,结果表明静电纺丝法制备的纯相BiFeO3纳米纤维表面粗糙有明显的颗粒感,呈一维棒管状结构,尺寸约为300 nm,这种纳米棒管结构具有更大的比表面积及更多的活性位点,能够提高BFO光催化降解效率。

铁酸铋  /  静电纺丝  /  响应面分析

Bismuth ferrite has become an effective semiconductor photocatalyst for the degradation of various wastewaters due to its narrow band gap, high chemical stability, and good visible light response. Pure phase BiFeO3 nanofibers were prepared by electrospinning method. The optimal degradation conditions for Congo Red were obtained through single factor experiments such as calcination temperature, PVP (polyvinyl pyrrolidone)concentration, collection distance, spinning voltage, and pushing speed. Four factors that significantly affect photocatalytic efficiency were selected for response surface analysis experiments with four factors and three levels. After optimization, the optimal PVP concentration was 12.17 wt%, collection distance was 14.07 cm, and spinning voltage was 12.03 kV The pushing speed is 0.74 μm/s, and under this condition, the efficiency of BiFeO3 photocatalytic degradation of Congo red can reach 90.43%. The phase analysis and morphology characterization of bismuth ferrite nanofibers were carried out using X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and Fourier transform infrared spectroscopy. The results show that the pure phase BFO nanofibers prepared by electrospinning has a rough surface and obvious particle sensation, presenting a one-dimensional rod-shaped structure with a size of about 300 nm. This nanorod-shaped structure has a larger specific surface area and more active sites, Can improve the photocatalytic degradation efficiency of BFO.

Bismuth ferrite  /  electrospinning  /  response surface analysis
李艳美, 张佳睿, 匡代洪, 阿瓦拜克力·肉苏里. 响应面法优化静电纺丝法制备BiFeO3的工艺研究. 科学技术与工程, 2025 , 25 (2) : 862 -870 . DOI: 10.12404/j.issn.1671-1815.2308910
Yan-mei LI, Jia-rui ZHANG, Dai-hong KUANG, Rousuli AWABAIKELI. Optimization of Electrospinning Process for Preparation of BiFeO3 by Response Surface Methodology[J]. Science Technology and Engineering, 2025 , 25 (2) : 862 -870 . DOI: 10.12404/j.issn.1671-1815.2308910
响应面法(response surface methodology,RSM)是一种试验设计和数学建模的统计多项式方法,是设计实验和优化过程的强大工具[1-2],优化过程参数可获得特定的目标函数。它能建立输入和输出变量之间的量化关系,在保持原料配比的同时提高效率和最小化成本。它能最大限度地减少特定因素及其水平的实验数量,同时可利用方差分析识别显著影响响应的因素,用回归方程来预测响应[3]。先前研究表明,RSM是一个顺序程序,可为混合优化提供最优值及评估其统计模型的关系[4]。Samson等[5]用RSM寻找电化学氧化工艺去除含盐废水的最佳参数。Sushanta等[6]借助它优化氢氟酸浓度、温度和时间参数,研究了煤的灰分还原。El等[7]使用它将液相和气相中的硫化物浓度与物理化学参数相关联,根据废水特征选择最合适的处理方法。Ahmed等[8]将其用于优化交联壳聚糖-三聚磷酸盐/TiO2纳米复合材料的合成条件及活性橙的吸附条件,并通过方差分析指出重要因素。总之,RSM已广泛用于食品[5]、农业[6]、化工[9-10]和环境[7-8,11]等领域。
静电纺丝的原理是在高压电场中喷射溶液、悬浮液或熔体,聚合物微小射流运行相当长的距离,最终固化成连续的微/纳米纤维[12]。基本装置由高压电源、喷丝头、收集器三部分组成。它是一种用于生成纳米级至微米级一维纤维材料的通用且可行的技术,具有原料来源广泛、工艺简单可控、纤维直径小和孔隙率高等优点[13]。过去20年,作为一种优势明显的新纳米材料制备技术来生产具有多样化成分、结构和特性的纳米纤维。新纳米技术意味着新的纳米结构,基于对复杂纳米结构的先进功能纳米材料的需求推动了多种静电纺丝工艺的出现[14]。目前静电纺丝在气过滤膜、多功能防护膜、催化剂的用途以及食品加工[15-20]等方面已发挥巨大作用,但该技术的潜在价值仍未完全开发。
铁酸铋(BiFeO3,BFO)是室温下唯一具有反铁磁性和铁电性的钙钛矿型单相多铁性材料,具有R3c空间群,直接带隙范围为2.2~2.8 eV,间接带隙范围为0.4~1.0 eV[21]。它在可见光照射下可降解废水,其无毒性、低成本和长期稳定性使其成为降解废水的有效光催化剂。
现以五水硝酸铋和九水硝酸铁为原料,聚乙烯吡咯烷酮(polyvinyl pyrrolidone,PVP)为助纺剂,无水乙醇和N,N-二甲基甲酰胺(N,N-Dimethylformamide,DMF)为溶剂制备BFO前纺液,再利用静电纺丝技术获得一维BFO纳米纤维。针对静电纺丝制备工艺的多个影响因素,利用Box-Benhnken响应面分析获得二阶响应面回归方程,利用Design-Expert8.0软件进行影响因子水平组合优化分析,对响应值进行探索,确定最佳制备工艺参数。在研究和优化制备BFO过程中的静电纺丝工艺的影响因素,RSM具有广阔的应用前景和巨大的发展潜力。现通过建立数学模型来描述多个影响因素与响应之间的关系,获得目标材料最佳的制备工艺,在高效率宏量制备纳米纤维方面具备很好的应用前景,并可以为工业化应用提供新思路和基础依据。
将同等摩尔比例的Fe(NO3)3·9H2O(Bi易挥发,过量添加10%[22])和Bi(NO3)3·5H2O在超声辅助下溶于5 mL乙二醇甲醚中,加入3 mL冰乙酸调节黏稠度,记作A溶液。另取7.5 mL DMF和4.5 mL无水乙醇混合,PVP浓度适量,充分搅拌至无色透明状,记作溶液B。将B溶液倒入A溶液,两者充分混合,在室温下搅拌12 h,形成均匀BFO前纺液。然后将前纺液装入5 mL注射器中,使用约0.5 mm的不锈钢喷丝头,在一定电压、推速、收集距离等条件下纺出均匀的纤维膜。最后将静电纺丝得到的纤维膜以3 ℃/min的升温速率,在马弗炉中煅烧保温2 h得到纯相BFO。
总之,BFO纤维的制备分为3个步骤:①以适当的聚合物浓度制备前体BFO溶液和静电纺丝溶液;②BFO前体溶液和静电纺丝溶液混合通过高压静电纺丝仪器获得无机/有机复合纤维;③退火纺丝纤维得到纯相BFO纤维。
在煅烧温度、PVP浓度、收集距离、纺丝电压和纺丝速度5组单因素实验基础上,设计4因子、3水平响应面试验。选择静电纺丝工艺过程中更显著的4个影响因子,分别为PVP浓度(A)、收集距离(B)、纺丝电压(C)、推料速度(D)和响应值为光催化效率(Y)进行设计,试验设计因素与水平如表1所示。通过Box-Benhnken响应面分析获得静电纺丝二阶响应面回归方程,利用Design-Expert8.0软件进行影响因子水平组合优化分析,对响应值进行预测,确定静电纺丝样品光催化降解污染物的最佳制备工艺参数。
将20 mg光催化剂放入50 mL(10 mg/L)的刚果红(Congo red,CR)溶液,在黑暗进行30 min到达吸附解吸平衡,再用600 W氙灯光照120 min,静置后取上清液在转速为9 000 r/min条件下离心5 min。最后,用紫外测量离心后的溶液得到紫外光谱的吸光度数值。光催化降解效率公式为
R= A 0 - A t A 0×100%
式(1)中:R为CR光催化效率;A0At分别为CR溶液初始和光照t时后的吸光度。
试验其他条件不变,选择煅烧温度为400、450、500、550、600 ℃进行研究,BFO光催化剂对CR光催化效率结果如图1所示。
煅烧是纳米材料性能优化和性能改性的重要处理工艺。在煅烧过程中,材料组合、分子排列和晶格都可能发生改变[23]。因此,优化煅烧条件是非常必要的。试验结果表明,当煅烧温度为550 ℃时候,光催化效率达到最高点,峰值为65.58%。温度低于最适温度时,随着温度的上升,光催化效率增大,因为随着温度的不断升高,更多的有机物质(如PVP)被去除,光催化剂的纯度更高;当温度超过一定界线时,光催化效率有所降低,这是因为温度超过最适温度时,分子排列和晶格可能发生了改变。
试验其他条件不变,选择PVP浓度为10、11、12、13、14 wt%进行研究,BFO对CR光催化效率结果如图2所示。
结果表明,当PVP浓度为12 wt%时,光催化效率峰值为81.82%。随着浓度增大光催化效率呈先增大后减小趋势,这是因为当PVP浓度较低时,聚合物分子链之间的缠结较少,导致纺丝液黏度较低,会形成串珠状纤维。珠状纤维的形成是由于射流在针尖和收集器之间未完全干燥。当PVP浓度增加时,大分子链之间的缠结增加,溶液黏度增加,这导致纺丝液从尖端延展到收集器之间所需时间增加。PVP浓度最佳时,溶剂有足够的时间挥发,射流中的聚合物分子链在凝固前拉伸更大,从而产生更均匀的纳米纤维。总之,当PVP浓度增到临界值时,同一电场对聚合物溶液拉伸形成均匀一维纳米纤维结构的效果最为显著。但聚合物浓度不能无限地增加,因为它会阻塞喷丝头针尖。
针尖到集电极的距离主要影响溶液中溶剂的蒸发和PVP分子链的拉伸,但也会引起电场强度的降低。为制造光滑均匀的静电纺丝纳米纤维,必须保持一个临界距离。目前大多数研究选择的收集距离固定在15 cm。试验其他条件不变,选择距离为11、12、13、14、15 cm进行研究,BFO对CR光催化效率结果如图3所示。
合适的飞行时间使溶剂蒸发是聚合物溶液射流的关键要求。结果表明,TCD为14 cm时,光催化效率达到79.52%。距离低于最佳值时,距离太短导致射流可能没有足够的时间干燥,溶剂未完全蒸发导致纤维熔融或扁平,可能形成熔合纤维。距离高于最佳值时,射流不稳定性增大易飞丝,纤维样品不均匀。总之,随着TCD增加,纳米纤维有足够的拉伸时间,溶剂有足够的时间蒸发。在相同电压条件下,随着TCD的增大,电场强度减小,射流不稳定性增大。因此,过高或过低的TCD都不利于获得最大值。
高电压产生的电场拉伸流体,球形液滴变形使其被迫形成一种非常特殊的形状,称为泰勒锥[24]。试验其他条件不变,选择纺丝电压为9、11、13、15、17 kV进行比较,BFO对CR光催化效率结果如图4所示。
当电压高于9 kV时,表面张力和黏滞力不再足以维持锥体,聚合物细射流被挤出并吸引到收集器表面回收。如图4所示,随着纺丝电压增大,光催化剂光催化效率先增大和减小。当电压为11 kV,光催化效率达到最高值90.88%,当电压15、17 kV时光催化效率相差不大。需要特别注意的是,11 kV时催化速率也最高,溶液在光照1 h时溶液已澄清,而其他样品在2 h才澄清。电压小于最佳值时,由于电场力较小难以克服纺丝液自身的表面张力形成喷射细流,导致泰勒锥难从圆锥尖端牵伸与分裂得到纤维细丝。在最佳电压之上,光催化效率降低的原因可能是电场在飞行时间内施加的加速度的影响,高电压值导致拉伸应力增加而导致纤维断裂[25]。此外,还存在将溶液射流分裂成多个射流的现象,这将导致纤维不均匀。
静电纺丝过程中,溶液的流速不仅影响泰勒锥的形成,而且影响纤维的形态。纳米纺丝的速率是由注射器推进速度来决定,调节纺丝速率的快慢不仅决定着纳米纤维的生产效率,更重要的是会影响针头液滴的稳定性。其他条件不变,选择速度为0.1、0.5、1、1.5、2 μm/s进行研究,BFO光催化剂对CR光催化效率结果如图5所示。
图5所示,当最佳速度为1 μm/s时,对CR光催化降解效率为88.9%,且速度对降解效率影响不大。低流速导致针尖内部出现真空,而高流速时,聚合物倾向于沉积在针尖边缘,这都会干扰泰勒锥的产生。对于每个纺丝电压,都需要一个最佳速率值来获得和保持稳定的泰勒锥。此外,射流需要稳定拉伸与分裂以避免形成缺陷,特别是珠状或串珠状纤维。根据质量守恒原理,低进料速率(流量)是静电纺丝的主要特点之一,流速是实现均匀纳米纤维或三维结构的关键因素[26]。超过临界距离会停止电纺丝或产生因弯曲不稳定性延长而产生的缺陷纤维,影响纤维分支。
在单因素试验基础上,选择PVP浓度(A)、收集距离(B)、纺丝电压(C)和推料速度(D)为自变量,选择光催化效率(Y)为因变量,运用Box-Benhnken响应面法对静电纺丝工艺制备BFO纳米纤维进行优化研究,四因素三水平试验设计共包含29组试验点,响应面法试验设计及对应响应值结果如表2所示。
表2进行统计分析,获得试验数据的方差分析(表3)。经多元回归拟合,模型的二次多项式方程为Y=88.01+2.21A+2.62B+5.17C-3.52D+0.73AB+2.08AC+0.64AD-3.21BC+2.14BD-4.04CD-8.61A2-3.42B2-7.35C2-5.35D2
表3所示,模型的P<0.01,表明该模型为极显著水平。失拟项为0.251 8>0.1,不显著,即该模型在回归区域内拟合度很好。相关系数R2=0.970 1,校正相关系数adjR2=0.940 1,adjR2-predR2=0.940 1-0.847 8<0.2,表明该试验模型拟合好。精密度=8.726>4,认为模型合理;CV=4.75%<10%,表明响应面回归模型精确可信。对光催化效率的影响显著的因素为AC、BD(P<0.05),对光催化效率的影响不显著因素为AB、AD,其他因素为极显著,表明试验因子与响应值之间不是简单的线性关系[27-28]。同时,模型F=32.39,模型的F值越大对试验影响越大[29]。可知,各个因素对光催化效率的影响程度为:C(纺丝电压)>D(推移速度)>B(收集距离)>A(PVP浓度)。
对回归方程Y进行响应面分析,AC交互项响应曲面图如图6所示。自变量因素AC对响应值Y的影响与响应面坡度陡峭程度成正相关;等高线呈椭圆形的程度与两个自变量交互作用的程度成正相关[30-31]。如图6所示,随着各因素数值的增大,光催化效率呈先增大后减少的趋势,响应曲面呈凸型陡峭曲面,交互项对应的等高线呈椭圆形,说明光催化效率存在极大值。
根据模型的回归方程分析获得最佳的静电纺丝工艺条件为:PVP浓度为12.17wt%、收集距离为14.07 cm、纺丝电压为12.03 kV、推移速度为 0.74 μm/s,在此条件下光催化效率理论可达到90.43%。考虑实验的可操作性,修正最佳条件为PVP浓度为12wt%、收集距离为14 cm、纺丝电压为12 kV、推移速度为0.5 μm/s,在此条件下,得出实际的光催化效率为88.78%,与预测值的相对误差为1.82%,说明该模型准确度良好。
利用X射线衍射进行BFO的物相分析,扫描速度为2(°)/min,扫描范围为10°~80°,XRD如图7所示。
XRD模式证明了静电纺丝制备的BFO纳米纤维具有R3c空间群(JCPDS#86—1518)的菱形结构,衍射峰分别位于22.41、31.75、32.06、38.95、39.48、45.75、51.31、51.73、56.36、56.96、57.16、66.34、67.06、71.34、75.59、76.11,峰值对应(012)、(104)、(110)、(006)、(202)、(024)、(116)、(122)、(018)、(214)、(300)、(208)、(220)、(036)、(128)、(134)晶体面[32-34]。BFO纳米纤维的衍射峰与标准PDF卡片几乎没有偏差,没有观察到与其他物质相对应的衍射峰,晶体结构索引良好,证明通过静电纺丝工艺成功制备了纯铁酸铋纳米纤维。
图8所示,最终的样品在SEM测试下呈现一维纳米棒的形貌。
图8所示,样品表面粗糙,有明显的颗粒感,纤维粗细不均匀,呈棒管状结构。纳米纤维无规则取向的交错排列在接收片上,同时由于经过高温煅烧,部分纤维断裂。纤维尺寸分布不均匀,尺寸约为300 nm。纳米棒管结构具有更大的比表面积及更多的活性位点,能够加快光生载流子的传输,从而提高光催化降解效率。
用拉曼光谱法研究了BFO纳米纤维结构,样品拉曼光谱如图9所示。根据晶体学族群理论预测知,R3c空间群中结晶的BiFeO3的活性拉曼模式标准方程为:Γ=4A1+9E[35],其中Γ为13个拉曼活性的振动模式,A1E代表菱面体扭曲结构[35]的对称性。
从图中可观察到BFO纳米纤维的9个拉曼活跃声子模式峰,3种光学横向A1对称和6种光学横向E对称声子模式。位于139、221、472 cm-1处的模式可分别划分为A1-1、A3-1和A4-1模式,拉曼光谱峰与z轴极化横向光学声子模式有关。位于74.6、255、362、525、610、711 cm-1的模式分别为E-1、E-2、E-3、E-4、E-6和E-7,与x-y平面偏振横向光学声子模式相关[36-37]。低频模式对应铋(Bi)和氧(O2)振动,高频模式对应Fe—O振动[38]。在139~362 cm-1范围或更高处内观察到的拉曼活性模与BFO的FeO6八面体中Fe和O2分子的原子运动有关[39-40]。在500 cm-1以上识别的拉曼活性模式归因于氧原子[41]的拉伸振动。
图10为BFO纳米纤维的典型FTIR光谱。FTIR光谱中各种官能团的存在是证明BFO纳米纤维形成的原因。
400~700 cm-1区域的三个吸收带与Bi—O和Fe—O的典型吸收带有关。409 cm-1处强峰与Bi—O振动模式有关。在549、705 cm-1处的另外两个峰是Fe—O键的拉伸振动[42]。1 384 cm-1处谱带表明存在硝酸盐离子[43-44]。此外,549 cm-1处的BiFeO3峰,对应于M—O(金属-氧)的晶格吸收,证实了BiFeO3的存在[45]。水和OH基团的反对称拉伸产生了从3 000~3 600 cm-1的宽吸收带,即3 454、3 631 cm-1处。
通过研究单因素试验考察静电纺丝工艺制备铁酸铋纳米纤维,利用Box-Benhnken 响应曲面分析法分析评价铁酸铋光催化降解阴离子染料的光催化效率,得到的最佳工艺参数为:PVP浓度为12.17 wt%、收集距离为14.07 cm、纺丝电压为12.03 kV、推移速度为0.74 μm/s,在此条件下光催化效率理论可达到90.43%。考虑实验的可操作性,修正为:PVP浓度为12 wt%、收集距离为14 cm、纺丝电压为12 kV、推移速度为0.5 μm/s,光催化效率为88.78%(±1.82%)。然后通过XRD、SEM、Raman、FTIR对样品进行了一系列物相分析和形貌表征,证实实验成功制备出了纯相铁酸铋纳米材料。BFO纳米纤维的衍射峰与标准PDF卡片几乎没有偏差,没有观察到与其他物质相对应的衍射峰。样品表面粗糙有颗粒感,呈棒管状结构,尺寸约为300 nm。纳米棒管结构具有更大的比表面积及更多的活性位点,能够加快光生载流子的传输效率,从而提高光催化降解效率。
  • 新疆维吾尔自治区自然科学基金(2022D01A201)
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doi: 10.12404/j.issn.1671-1815.2308910
  • 接收时间:2023-11-13
  • 首发时间:2025-12-05
  • 出版时间:2025-01-18
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  • 收稿日期:2023-11-13
  • 修回日期:2024-10-18
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新疆维吾尔自治区自然科学基金(2022D01A201)
作者信息
    1 新疆农业大学资源与环境学院, 乌鲁木齐 830052
    2 新疆农业大学数理学院, 乌鲁木齐 830052

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* 匡代洪(1977—),男,汉族,湖南衡阳人,博士,副教授,硕士研究生导师。研究方向:环境功能材料的制备及性能。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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