创新背景

防辐射玻璃是指具有防护如x射线、γ射线等放射性射线功能的特种玻璃。放射性射线属于高能量的电磁波，在通过不同的介质时，会导致介质的原子发生电离现象。电磁波波长越短，穿透能力越强。

由于射线的波长与原子的大小属于同一数量级，所以分子吸收系数，可由组成分子的各元素的原子吸收系数相加而得。因此，组成玻璃的各种氧化物的质量吸收系数可按这一原理算出。原子能工业和医学领域的放射性射线问题突出，对于防辐射玻璃的需求也比较大。玻璃吸收强放射线后容易变色，失去透明性，化学性质不够稳定，需要不断增强耐辐射性能。

创新过程

2020年11月，俄罗斯乌拉尔联邦大学的科研人员研发出新型的防辐射玻璃材料，具有优秀的屏蔽防辐射能力，防护辐射效果比现有的防辐射玻璃优秀3倍，吸收光子辐射的能力更强。

传统的防辐射玻璃主要使用防辐射的混凝土和铅，新型玻璃使用了新材料，并采用蒙特卡罗算法和其他计算机建模方法来模拟电离辐射的随机影响。

蒙特卡罗方法是指使用随机数或更常见的伪随机数来解决很多计算问题的方法，以概率统计理论为基础。使用这种算法进行计算机建模，对于电离辐射成果预测更加准确，能更好地开发玻璃的防辐射性能。

制造玻璃使用的材料的质量衰减系数、十倍衰减层、自由行程长度及其他辐射特性等方面的参数都比传统材料的参数大，材料厚度需要仅需传统产品的三分之一，提高性能的同时节约成本。

材料开发所需的原料和计算机建模都需要更加深入研究，使用新材料并深化算法符合原料要求，能够促进材料科学和科技发展。

创新关键点

深化算法利用新材料开发优质性能的防辐射玻璃。

Deepen the algorithm to simulate ionizing radiation and develop new radiation-proof glass

In November 2020, researchers from the Ural Federal University in Russia developed a new type of radiation protection glass material, which has excellent shielding and radiation protection capabilities. The radiation protection effect is 3 times better than the existing radiation protection glass, and the ability to absorb photon radiation is stronger.
While traditional radiation-resistant glass mainly uses radiation-resistant concrete and lead, the new glass uses new materials and uses Monte Carlo algorithms and other computer modeling methods to simulate the random effects of ionizing radiation. Monte Carlo methods refer to methods of solving many computational problems using random numbers or, more commonly, pseudo-random numbers, based on the theory of probability and statistics. Using this algorithm for computer modeling, the prediction of ionizing radiation results is more accurate, and the radiation protection performance of glass can be better developed.
The parameters of the mass attenuation coefficient, ten times attenuation layer, free travel length and other radiation characteristics of the materials used in the manufacture of glass are larger than those of the traditional materials, and the material thickness needs to be only one-third of the traditional products. At the same time save costs.
Both the raw materials and computer modeling required for material development require more in-depth research. Using new materials and deepening algorithms to meet raw material requirements can promote the development of materials science and technology.