Article(id=1190369067291738256, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1190365079976640655, articleNumber=null, orderNo=null, doi=10.16790/j.cnki.1009-9239.im.2025.07.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1740067200000, receivedDateStr=2025-02-21, revisedDate=1741536000000, revisedDateStr=2025-03-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1761735701629, onlineDateStr=2025-10-29, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761735701629, onlineIssueDateStr=2025-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761735701629, creator=13701087609, updateTime=1761735701629, updator=13701087609, issue=Issue{id=1190365079976640655, tenantId=1146029695717560320, journalId=1149653034449285133, year='2025', volume='58', issue='7', pageStart='1', pageEnd='150', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1761734750980, creator=13701087609, updateTime=1761735924323, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1190370001430348320, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1190365079976640655, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1190370001430348321, tenantId=1146029695717560320, journalId=1149653034449285133, issueId=1190365079976640655, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=79, endPage=85, ext={EN=ArticleExt(id=1190369067627282579, articleId=1190369067291738256, tenantId=1146029695717560320, journalId=1149653034449285133, language=EN, title=Research on computer neural network-aided design of insulating glass formulation and coating properties, columnId=1190369066675179686, journalTitle=Insulating Materials, columnName=Material Research, runingTitle=null, highlight=

Thick-film heating has become a key thermal-management solution for new-energy vehicles. To meet the relevant application demands, it is necessary to develop dielectric slurries for aluminum-based thick-film heating elements. This study utilized the built-in machine learning model of the Inorganic Glass Engineer System for property prediction to assist in the development of dielectric insulating glass formulations for aluminum-based thick-film heating elements, and conducted experimental verification. The results show that the insulating glass prepared by the optimal formula can be sintered at 580℃, with a thermal expansion coefficient of 18.8×10-⁶℃-1. When the dielectric-layer thickness exceeds 110 μm, it has a breakdown voltage over 1.29 kV and a leakage current less than 0.21 mA, which can meet the usage requirements of the medium layer of aluminum-based thick-film heating elements.

, articleAbstract=

Thick-film heating has become a key thermal-management solution for new-energy vehicles. To meet the relevant application demands, it is necessary to develop dielectric slurries for aluminum-based thick-film heating elements. This study utilized the built-in machine learning model of the Inorganic Glass Engineer System for property prediction to assist in the development of dielectric insulating glass formulations for aluminum-based thick-film heating elements, and conducted experimental verification. The results show that the insulating glass prepared by the optimal formula can be sintered at 580℃, with a thermal expansion coefficient of 18.8×10-⁶℃-1. When the dielectric-layer thickness exceeds 110 μm, it has a breakdown voltage over 1.29 kV and a leakage current less than 0.21 mA, which can meet the usage requirements of the medium layer of aluminum-based thick-film heating elements.

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厚膜加热已成为新能源汽车热管理的主要应用方案之一。为满足新能源汽车热管理的应用需求,有必要开发可用于铝基厚膜加热元件的介质浆料。本文利用无机玻璃工程师系统内置的机器学习模型进行性质预测,辅助铝基厚膜加热元件介质层绝缘玻璃的配方开发,并进行实验验证。结果表明:最优配方制备的玻璃可在580℃下烧结,热膨胀系数为18.8×10-6-1,当介质层厚度>110 μm时,其击穿电压>1.29 kV,泄漏电流小于0.21 mA,可满足铝基厚膜加热元件介质层的使用要求。

, articleAbstract=

厚膜加热已成为新能源汽车热管理的主要应用方案之一。为满足新能源汽车热管理的应用需求,有必要开发可用于铝基厚膜加热元件的介质浆料。本文利用无机玻璃工程师系统内置的机器学习模型进行性质预测,辅助铝基厚膜加热元件介质层绝缘玻璃的配方开发,并进行实验验证。结果表明:最优配方制备的玻璃可在580℃下烧结,热膨胀系数为18.8×10-6-1,当介质层厚度>110 μm时,其击穿电压>1.29 kV,泄漏电流小于0.21 mA,可满足铝基厚膜加热元件介质层的使用要求。

, correspAuthors=null, authorNote=null, correspAuthorsNote=
陈彩明(1980-),女(汉族),湖南邵阳人,工程师,主要从事计算机仿真计算以及纳米光电材料、器件制备和仿真计算的研究。
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蔡浩然(1999-),男(汉族),四川简阳人,硕士生,主要从事低熔点玻璃的制备与性能测试的研究;

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蔡浩然(1999-),男(汉族),四川简阳人,硕士生,主要从事低熔点玻璃的制备与性能测试的研究;

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蔡浩然(1999-),男(汉族),四川简阳人,硕士生,主要从事低熔点玻璃的制备与性能测试的研究;

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样品 SiO2 Na2O TiO2 V2O5 K2O Sb2O3 其他
N1 35.8 15.4 15.4 8.2 15.8 4.5 4.9
N2 34.2 17.0 15.4 8.2 15.8 4.5 4.9
N3 32.6 18.6 15.4 8.2 15.8 4.5 4.9
N4 31.0 20.2 15.4 8.2 15.8 4.5 4.9
N5 29.4 21.8 15.4 8.2 15.8 4.5 4.9
N6 27.8 23.4 15.4 8.2 15.8 4.5 4.9
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铝基板介质层玻璃配方

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样品 SiO2 Na2O TiO2 V2O5 K2O Sb2O3 其他
N1 35.8 15.4 15.4 8.2 15.8 4.5 4.9
N2 34.2 17.0 15.4 8.2 15.8 4.5 4.9
N3 32.6 18.6 15.4 8.2 15.8 4.5 4.9
N4 31.0 20.2 15.4 8.2 15.8 4.5 4.9
N5 29.4 21.8 15.4 8.2 15.8 4.5 4.9
N6 27.8 23.4 15.4 8.2 15.8 4.5 4.9
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样品 Tg/℃ α/(×10-6-1)
N1 475 16.74
N2 461 16.88
N3 444 18.24
N4 434 18.47
N5 418 19.61
N6 411 19.74
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预测得到玻璃的热学性能

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样品 Tg/℃ α/(×10-6-1)
N1 475 16.74
N2 461 16.88
N3 444 18.24
N4 434 18.47
N5 418 19.61
N6 411 19.74
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样品 Tg/℃ Tx/℃ Tc/℃ ΔT/℃ α/(×10-6-1)
N1 477 16.2
N2 460 571 111 16.8
N3 450 546 96 17.4
N4 423 529 108 18.2
N5 395 496 101 18.8
N6 370 455 537 85 17.1
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样品的特征温度与热膨胀系数

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样品 Tg/℃ Tx/℃ Tc/℃ ΔT/℃ α/(×10-6-1)
N1 477 16.2
N2 460 571 111 16.8
N3 450 546 96 17.4
N4 423 529 108 18.2
N5 395 496 101 18.8
N6 370 455 537 85 17.1
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丝印次数 膜厚度/μm 绝缘电阻/Ω 泄漏电流/mA 击穿电压/kV
1 38 >105 0.29 0.71
2 71 >105 0.26 0.83
3 110 >106 0.21 1.31
4 145 >107 0.18 1.76
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介质层的绝缘性能

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丝印次数 膜厚度/μm 绝缘电阻/Ω 泄漏电流/mA 击穿电压/kV
1 38 >105 0.29 0.71
2 71 >105 0.26 0.83
3 110 >106 0.21 1.31
4 145 >107 0.18 1.76
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机器学习辅助设计绝缘玻璃配方及涂层性能研究
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蔡浩然 1, 3, 4 , 陈彩明 2 , 龙神峰 1, 3, 4 , 丁静 1, 3, 4 , 朱归胜 1, 3, 4 , 徐华蕊 1, 3, 4 , 谢婉卿 1
绝缘材料 | 材料研究 2025,58(7): 79-85
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绝缘材料 | 材料研究 2025, 58(7): 79-85
机器学习辅助设计绝缘玻璃配方及涂层性能研究
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蔡浩然1, 3, 4, 陈彩明2, 龙神峰1, 3, 4, 丁静1, 3, 4, 朱归胜1, 3, 4, 徐华蕊1, 3, 4, 谢婉卿1
作者信息
  • 1.桂林电子科技大学,材料科学与工程学院,广西 桂林 541004
  • 2.桂林电子科技大学,信息与通信学院,广西 桂林 541004
  • 3.电子信息材料与器件教育部工程研究中心,广西 桂林 541004
  • 4.广西信息材料重点实验室,广西 桂林 541004
  • 蔡浩然(1999-),男(汉族),四川简阳人,硕士生,主要从事低熔点玻璃的制备与性能测试的研究;

通讯作者:

陈彩明(1980-),女(汉族),湖南邵阳人,工程师,主要从事计算机仿真计算以及纳米光电材料、器件制备和仿真计算的研究。
Research on computer neural network-aided design of insulating glass formulation and coating properties
Haoran CAI1, 3, 4, Caiming CHEN2, Shengfeng LONG1, 3, 4, Jing DING1, 3, 4, Guisheng ZHU1, 3, 4, Huarui XU1, 3, 4, Wanqing XIE1
Affiliations
  • 1. School of Materials Science and Engineering, Guilin University of Electronic Technology, Guilin 541004, China
  • 2. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
  • 3. Electrical Information Materials and Devices Engineering Research Center of Education, Guilin 541004, China
  • 4. Guangxi Key Laboratory of Information Materials, Guilin 541004, China
出版时间: 2025-07-20 doi: 10.16790/j.cnki.1009-9239.im.2025.07.009
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厚膜加热已成为新能源汽车热管理的主要应用方案之一。为满足新能源汽车热管理的应用需求,有必要开发可用于铝基厚膜加热元件的介质浆料。本文利用无机玻璃工程师系统内置的机器学习模型进行性质预测,辅助铝基厚膜加热元件介质层绝缘玻璃的配方开发,并进行实验验证。结果表明:最优配方制备的玻璃可在580℃下烧结,热膨胀系数为18.8×10-6-1,当介质层厚度>110 μm时,其击穿电压>1.29 kV,泄漏电流小于0.21 mA,可满足铝基厚膜加热元件介质层的使用要求。

介质浆料  /  绝缘玻璃  /  铝基厚膜加热元件  /  机器学习

Thick-film heating has become a key thermal-management solution for new-energy vehicles. To meet the relevant application demands, it is necessary to develop dielectric slurries for aluminum-based thick-film heating elements. This study utilized the built-in machine learning model of the Inorganic Glass Engineer System for property prediction to assist in the development of dielectric insulating glass formulations for aluminum-based thick-film heating elements, and conducted experimental verification. The results show that the insulating glass prepared by the optimal formula can be sintered at 580℃, with a thermal expansion coefficient of 18.8×10-⁶℃-1. When the dielectric-layer thickness exceeds 110 μm, it has a breakdown voltage over 1.29 kV and a leakage current less than 0.21 mA, which can meet the usage requirements of the medium layer of aluminum-based thick-film heating elements.

dielectric slurry  /  insulating glass  /  aluminum-based thick-film heating element  /  machine-learning
蔡浩然, 陈彩明, 龙神峰, 丁静, 朱归胜, 徐华蕊, 谢婉卿. 机器学习辅助设计绝缘玻璃配方及涂层性能研究. 绝缘材料, 2025 , 58 (7) : 79 -85 . DOI: 10.16790/j.cnki.1009-9239.im.2025.07.009
Haoran CAI, Caiming CHEN, Shengfeng LONG, Jing DING, Guisheng ZHU, Huarui XU, Wanqing XIE. Research on computer neural network-aided design of insulating glass formulation and coating properties[J]. Insulating Materials, 2025 , 58 (7) : 79 -85 . DOI: 10.16790/j.cnki.1009-9239.im.2025.07.009
近年来,我国新能源汽车市场快速发展,而新能源汽车的性能、安全和续航能力很大程度取决于热管理技术的应用[1],对于高效、稳定加热器的需求日益迫切。
新能源汽车热管理主要采用热泵、正温度系数热敏电阻(positive temperature coefficient thermistor,PTC)和厚膜加热等电加热方式[2]。随着新能源汽车性能不断提高,800 V高电压平台成为电车发展的趋势,虽然热泵与PTC加热技术成熟度高,但是热泵的成本较高,且在-20℃的低温环境下近乎不工作;PTC技术虽然简单但不稳定且耗电量大,高电压下有被击穿的潜在风险。厚膜加热技术通过在基板上印刷烧结得到膜层,通过通电发热,实现电热转换。厚膜加热器兼具加热效率高、工作电压范围宽和成本低廉等优势,可为电动汽车热管理系统提供更具性价比的解决方案。而厚膜加热元件上的绝缘玻璃介质层对其使用性能起着决定性的作用。
金属铝具有导热系数高、质量轻、延展性好和易加工等特点,是新能源汽车热管理厚膜加热元件的理想基板材料。当前适用于铝基介质层的低熔点玻璃多为含铅玻璃,伴随着环保法规的限制,无铅低熔点玻璃的开发已经十分明确[3]。国内外针对适用于铝金属的无铅厚膜绝缘玻璃的研究多以专利的形式发表,且集中在磷酸盐、钒酸盐玻璃体系[4]。SHI X Z等[5]使用熔融淬火法制备了热膨胀系数高达17.7×10-6-1的磷酸盐玻璃。WANG F等[6]制备的V2O5-B2O3-TeO2玻璃热膨胀系数最高可达18.1×10-6-1,软化温度低至480℃。磷酸盐、钒酸盐玻璃的主要问题是化学稳定性与热膨胀系数之间的平衡取舍,相比之下硅酸盐玻璃拥有更好的化学稳定性[4,7]。刘建等[8]制备了一种SiO2-Bi2O3-B2O3-Al2O3低熔点玻璃粘结剂,可用作铝基介质浆料,具备良好的施工性能,热膨胀系数可达18×10-6-1,具有良好的绝缘性能,击穿电压高,可用于硅酸盐玻璃体系进行绝缘玻璃配方的设计。
对于绝缘玻璃成分的设计所涉及的科学和工程知识较复杂,需要进行大量重复实验,容易造成资源的浪费,因此许多学者开始研究使用计算机辅助进行玻璃配方设计[9-11]。机器学习算法能够处理复杂的非线性关系,其辅助设计能力在各领域逐渐得到应用。张闫等[12]综述了机器学习在钛合金设计的应用进展,指出有效的机器学习模型有利于材料高效、精确的研发;G M KONTOGEORGIS等[13]在综述中总结得到,采用计算机辅助产品配方设计可以有效筛选复杂配方,加快产品的开发进程,并指出几乎所有计算机辅助设计(CAD)得到的结果都需要与实验数据进行核对。在应用方面,张增辉等[14]提出了一种基于机器学习算法的变压器油健康状态评估方法;仲陆祎等[15]通过构建预测模型对复合吸波材料的磁导率进行了预测。
本研究利用无机玻璃工程师系统内置的机器学习模型[16]进行铝基厚膜绝缘玻璃配方设计,采用“计算预测-性能验证”的玻璃配方研发模式,制备得到玻璃粉并在铝基表面制备介质层,并对粉体及介质层性能进行研究,以加快玻璃配方研发周期,为快速开发新型功能玻璃材料提供新的研发思路。
无机玻璃工程师系统(GES)软件结构如图1所示。GES以干福熹等[11]的无机玻璃物理性质计算体系作为主要理论依据,其内置的DDNL可根据需要进行无机玻璃的热学、力学性质等多种物理性质的预测。因此,GES可以进行玻璃配方的设计优化和管理[17]
所设计的铝基板介质层玻璃配方如表1所示,利用GES进行玻璃配方性能预测得到的玻璃化转变温度(Tg)与热膨胀系数(α)如表2所示。
根据玻璃配方采用传统的熔融法制备玻璃样品,实验原料均为西陇化工有限公司生产,纯度为分析纯。
玻璃粉样品及介质层的制备流程如图2所示。按表1所设计的配方称取原料,充分混合后得到配合料;将配合料装入氧化铝坩埚并放入升降炉,升温至1 250℃,保温2 h使玻璃液澄清;将部分玻璃液倒入去离子水中水淬,经破碎、球磨、干燥后得到粒径为5~15 μm的玻璃粉;另一部分熔融玻璃液倒入预热的石墨模具中成型,再迅速放入450℃马弗炉中退火,随炉冷却后切割成边长为1.5 cm的玻璃块,对得到的样品进行测试表征。
通过测试筛选出性能符合使用要求的玻璃粉,与有机溶剂经辊轧、脱泡制备得到介质浆料,并在铝基板上通过丝印、烧结得到玻璃绝缘介质层。
采用差示扫描量热仪(DSC,STA-449F3型,Netzsch公司)测定玻璃粉的玻璃化转变温度(Tg)、软化温度(Tf)、初始结晶温度(Tx)和结晶峰值温度(Tc);采用热膨胀系数分析仪(DIL 402EP型,耐驰公司)测定玻璃块的热膨胀系数(α);采用X-射线衍射分析仪(XRD,Smart Lab型,上海力晶科学仪器公司)分别表征水淬及580℃保温1 h后玻璃粉的晶相结构。使用化学失重法评估玻璃的化学稳定性,将块状样品分别浸泡在质量分数为5%的HCl溶液和5%的NaOH溶液中腐蚀24 h,之后用去离子水清洗样品表面,干燥并称重,然后通过式(1)计算玻璃块的质量损失速率。
D R = Δ W S t
式(1)中:DR表示质量损失速率,g·cm-2·min-1;ΔW为损失的质量,g;S为面积,cm2t为玻璃块浸没在溶液中的时间,min。
采用光学显微镜对介质层表面显微结构进行观察,采用扫描电子显微镜(Quanta 450 FEG型,FEI公司)分析介质层截面微观形貌。采用耐电压测试仪(OCT101型,青岛海思伟创电子科技有限公司),根据GB 4706.1—2024测试绝缘电阻和泄漏电流,根据GB 1408.1—2016测试击穿电压。
图3(a)为样品N1~N6的DSC曲线,总结的特征温度如表3所示。图3(b)、(c)分别为样品N1~N6的玻璃化转变温度(Tg)、热膨胀系数(α)计算值与实验测试值。从图3(a)可以看出,随着配方中Na2O含量的提高,玻璃的TgTx逐渐降低。对比图3(b)、(c)Tgα的实测值与理论计算值,发现两者在变化趋势上一致性良好。样品N1的Tg实测值与计算值差值最小,仅为2℃,样品N6的Tg实测值与计算值差值最大,为41℃。随着配方中Na2O含量的提高,样品的α整体增大,但样品N6的α实测值(17.1×10-6-1)较计算值(19.74×10-6-1)出现了明显的下降,且在DSC曲线上出现明显的析晶峰。这可能是由于升温过程中玻璃体中析出了晶体导致的。碱金属氧化物的加入在玻璃网络中起到“断网”的作用[18],会使得玻璃网络结构更加松弛,从而降低玻璃的稳定性,更易析晶。
玻璃的初始析晶温度与玻璃化转变温度的差值ΔT可以进一步衡量玻璃的热稳定性,ΔT的值越大,表示玻璃的热稳定性越好。
表3可以看出,样品N6的ΔT为85℃,小于其他样品,且DSC曲线在537℃出现了明显的结晶峰,表明样品N6的热稳定性最差,烧结过程中出现了晶体的析出,这可能会对玻璃的性能造成影响。图3(c)中样品N1~N5热膨胀系数的变化是由于Na2O的键能较小,抵抗热振动的能力较弱[19-20]。玻璃相的结构发生变化可能是N6的热膨胀系数预测值较实测值出现明显下降的原因,需进一步通过测试分析其原因。
上述结果说明,Na2O的添加使得玻璃网络完整性降低,在Tg降低、α增大的同时,也会导致玻璃样品在烧结的过程中更易析出晶体。
图4(a)、(b)分别为玻璃样品在水淬与580℃热处理后的XRD谱图。从图4(a)可以看出,样品N1~N6均呈现出典型的玻璃相衍射特征,说明水淬后制备的玻璃均为非晶相的无定形态。从图4(b)可以看出,热处理后,样品N6出现了明显的Na2Zn(SiO4)晶体衍射峰,而样品N1~N5仍然以非晶玻璃相为主。这是由于随着Na2O含量的增加,玻璃的网络结构变得疏松,促进了高温下硅酸盐基团的扩散,使得晶体易于析出[21],进而导致玻璃样品在介质层烧结过程中黏度和软化温度大幅升高,不利于介质层的致密化。故不希望烧结过程中玻璃产生大量的析晶。
XRD测试结果表明,Na2O含量的增加会使得玻璃体系的热稳定性降低,析晶倾向增加。对比热学性能,晶体析出后会使得玻璃体晶相结构发生改变,热膨胀系数降低,说明GES软件在预测玻璃的热膨胀系数时无法考虑到热处理对晶相结构变化的影响,这也证明了样品N6热膨胀系数的突变是由于热处理过程中晶体析出导致的。在使用GES进行性能预测时需要同时考虑玻璃晶体结构变化对性能的影响,同时,在使用软件理论预测指导实际生产的过程中需要在关键玻璃配方上进行实验,以验证其可行性。
图5为样品N1~N6分别在5%HCl与5%NaOH中浸泡24 h的质量损失速率。
图5可以看出,样品的质量损失速率均随着Na2O含量的增加而增大。玻璃的化学稳定性与其结构的完整性和组分的溶出能力有关[22]。在酸性条件下,样品N1~N6的质量损失速率随着Na2O的含量增加,从2.05×10-7 g·cm-2·min-1增加到351.63×10-7 g·cm-2·min-1,这是由于在酸溶液中玻璃样品产生 Si-OH和NaOH(反应式如式(2)所示),而NaOH与酸溶液中的H+不断反应,使得玻璃的溶解不断发生。
S i - O - N a + + H + O H - S i - O H + N a O H
碱溶液对硅氧骨架(≡Si-O-Si≡)的侵蚀使Si-O键断裂,网络解体产生Si-O-,Na2O含量的增加使得网络结构变得疏松,进一步加剧了玻璃骨架的溶解[23],致使玻璃溶解在碱液中产生质量损失[24],因此样品N1~N6在碱溶液中的质量损失速率从5.32×10-7 g·cm-2·min-1增大到167.43×10-7 g·cm-2·min-1
图5还可以看出,样品N1、N2具有最好的化学稳定性,失重速率小于10×10-7 g·cm-2·min-1,样品N6的化学稳定性最差,在酸性条件下其平均质量损失速率为351.63×10-7 g·cm-2·min-1。样品N6无论在酸性还是碱性条件下,其质量损失速率均有明显的增加,这可能是由于Na2Zn(SiO4)晶体析出导致玻璃的化学稳定性降低。
对用于铝基板上的绝缘玻璃而言,烧结温度与热膨胀系数的匹配是衡量介质层玻璃使用性能好坏的关键指标之一。从样品N1~N6预测结果与实际测试性能来看,样品N1、N2作为铝基板介质层时,玻璃的热膨胀系数过小,并且玻璃化转变温度偏高;样品N6的热稳定性与化学稳定性较低,在烧结温度下易发生析晶,导致烧结时粉体无法致密化,影响介质层性能,不适合作为介质浆料使用。因此,选择样品N3~N5进行玻璃绝缘介质层的制备与测试。
图6为样品N3~N5作为玻璃相的介质浆料在铝基板表面得到的介质层表面光学显微图与局部放大图。从图6(a)可以看出,样品N3形成的介质层表面极不光滑,放大后表面布满裂纹。这由于样品N3的热膨胀系数只有17.4×10-6-1,与铝基板的热膨胀系数(22.1×10-6-1)不够匹配,在烧结冷却过程中介质层与基板的收缩率不同,导致介质层与基板之间产生机械应力与热应力,使得介质层表面产生龟裂状裂纹。
图6(b)、(c)可以看出,以样品N4、N5为玻璃相的介质层缺陷明显减少,表面平整度显著提高,这是由于样品N4、N5的热膨胀系数分别为18.2×10-6-1、18.8×10-6-1,与铝基板更加匹配。其中N5介质层表面没有明显的因应力不均而产生的裂纹,且经过3次印刷烧结后表面仍光滑致密。而N4介质层表面放大后可以发现条状纹路与针孔缺陷,可能是由于玻璃相的熔点过高,介质层在烧结时无法流平,后续丝印烧结无法进一步致密化填补“稀释”这些缺陷。
图7为样品N3~N5作为玻璃相的介质浆料在铝基板表面形成的介质层截面SEM图。从图7(a)、(b)可以看出,N3与N4玻璃绝缘层与铝基板之间存在脱落现象。这是因为在烧结过程中介质层与铝基板的热膨胀系数不匹配,产生了应力;玻璃的玻璃化转变温度过高,使得玻璃与基板无法充分润湿,两个因素导致结合层的脱落。从图7(c)可以看出,N5玻璃绝缘层与铝基板界面完整,结合良好。说明当体系中Na2O的质量分数为21.8%时,介质浆料可以满足用作铝基板绝缘层的需求,这与软件预测的趋势基本吻合。
使用N5玻璃配方制备介质浆料,在铝基板上多次丝印烧结得到玻璃绝缘介质层,测试得到其电绝缘性能数据如表6所示。从表6可以看出,介质层的电绝缘性能随着厚度的增加而提高,介质层越厚,其绝缘电阻越大,泄漏电流越小,击穿电压越高。依据GB/T 4706.1—2024《家用和类似用途电器的安全 第1部分:通用要求》的通用要求,介质层在500 V的电压下,绝缘电阻应大于104 Ω;在1.06倍的额定电压下,泄漏电流不应超过0.75 mA。为了保证铝基板表面制备的介质层击穿电压满足GB/T 1408.1—2016《绝缘材料 电气强度试验方法 第1部分:工频下试验》中大于1 250 V的要求,需进行3次丝印烧结使膜厚大于110 μm。
(1)利用GES软件计算多成分硅酸盐玻璃的性能,用于配方的辅助筛选,实验结果表明软件可预测配方的性能变化趋势,但仍需实验进行性能验证,实验表明“计算预测-性能验证”的开发模式可缩短配方开发周期。
(2)成功开发了适用于铝合金基板的绝缘玻璃配方,当Na2O在体系中的质量分数为21.8%时,制备的浆料在铝基板上经过3次丝印烧结,制得的介质层膜厚大于110 μm时,击穿电压大于1 250 V,相关电绝缘性能测试均满足相关国标要求,可用于铝基厚膜加热元件的介质层绝缘玻璃。
  • 广西科技基地与人才专项(AD23023013)
  • 桂林市科学研究与技术开发计划项目(20220120-1)
  • 广西重大科技专项(AA21077018)
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doi: 10.16790/j.cnki.1009-9239.im.2025.07.009
  • 接收时间:2025-02-21
  • 首发时间:2025-10-29
  • 出版时间:2025-07-20
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  • 收稿日期:2025-02-21
  • 修回日期:2025-03-10
基金
广西科技基地与人才专项(AD23023013)
桂林市科学研究与技术开发计划项目(20220120-1)
广西重大科技专项(AA21077018)
作者信息
    1.桂林电子科技大学,材料科学与工程学院,广西 桂林 541004
    2.桂林电子科技大学,信息与通信学院,广西 桂林 541004
    3.电子信息材料与器件教育部工程研究中心,广西 桂林 541004
    4.广西信息材料重点实验室,广西 桂林 541004

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陈彩明(1980-),女(汉族),湖南邵阳人,工程师,主要从事计算机仿真计算以及纳米光电材料、器件制备和仿真计算的研究。
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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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