Boosting the Thermoelectric Performance of the Doped DPP-EDOT Conjugated Polymer by Incorporating an Ionic Additive

The thermoelectric (TE) performance of organic materials is limited by the coupling of Seebeck coefficient and electrical conductivity. Herein a new strategy is reported to boost the Seebeck coefficient of conjugated polymer without significantly reducing the electrical conductivity by incorporation of an ionic additive DPPNMe₃Br . The doped polymer PDPP - EDOT thin film exhibits high electrical conductivity up to 1377 ± 109 S cm⁻¹ but low Seebeck coefficient below 30 µV K⁻¹ and a maximum power factor of 59 ± 10 µW m⁻¹ K⁻². Interestingly, incorporation of small amount (at a molar ratio of 1:30) of DPPNMe₃Br into PDPP - EDOT results in the significant enhancement of Seebeck coefficient along with the slight decrease of electrical conductivity after doping. Consequently, the power factor (PF) is boosted to 571 ± 38 µW m⁻¹ K⁻² and ZT reaches 0.28 ± 0.02 at 130 °C, which is among the highest for the reported organic TE materials. Based on the theoretical calculation, it is assumed that the enhancement of TE performance for the doped PDPP - EDOT by DPPNMe₃Br is mainly attributed to the increase of energetic disorder for PDPP - EDOT .     

A multiobjective topology optimization approach for cost and time minimization in additive manufacturing

The ever-present drive for increasingly high-performance designs realized on shorter timelines has fostered the need for computational design generation tools such as topology optimization. However, topology optimization has always posed the challenge of generating difficult, if not impossible to manufacture designs. The recent proliferation of additive manufacturing technologies provides a solution to this challenge. The integration of these technologies undoubtedly has the potential for significant impact in the world of mechanical design and engineering. This work presents a new methodology which mathematically considers additive manufacturing cost and build time alongside the structural performance of a component during the topology optimization procedure. Two geometric factors, namely, the surface area and support volume required for the design, are found to correlate to cost and build time and are controlled through the topology optimization procedure. A novel methodology to consider each of these factors dynamically during the topology optimization procedure is presented. The methodology, based largely on the use of the spatial gradient of the density field, is developed in such a way that it does not leverage the finite element discretization scheme. This work investigates a problem that has not yet been explored in the literature: direct minimization of support material volume in density-based topology optimization. The entire methodology is formulated in a smooth and differentiable manner, and the sensitivity expressions required by gradient based optimization solvers are presented. A series of example problems are provided to demonstrate the efficacy of the proposed methodology.     

Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

Designing future‐proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial‐and‐error process, as this limits the search for untapped regions of the solution space. Here, a computational data‐driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and supercompressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data‐driven code is available to facilitate future design and analysis of metamaterials and structures ( https://github.com/mabessa/F3DAS ).     

The study of the effect of external additives on the synthesis of fumed TiO2 with a high content of rutile structure by the novel natural dropping thermal treatment

The effect of external additives on the synthesis of fumed TiO₂ with a high rutile structure content was studied. The focus of this investigation was on the external additive species, agglomerates of the fumed TiO₂ before and after the thermal treatment. The transformation ratio from the anatase to rutile structure of the thermally-treated fumed TiO₂ was investigated as a function of the fumed TiO₂ agglomerate before the thermal treatment. Small agglomerated powders resulted in a decrease of transformation temperature. Two novel results were obtained in this investigation. One was a new fumed TiO₂ with a 100% rutile structure having an excellent dispersibility being successfully synthesized by the thermal treatment of AEROXIDE® TiO₂ P 25 with a small portion of AEROSIL® R 972. The other was the remarkable acceleration of the transformation from the anatase to rutile structure and grain growth/sintering of the thermally-treated fumed TiO₂ observed at a relatively low thermal treatment temperature by the oxidation reaction of calcium stearate as an external additive.     

谐和与高斯白噪声联合作用下二自由度系统的随机稳定性研究

主要研究了谐和与高斯白噪声共同作用下二自由度系统的随机稳定性问题.首先,通过扩维的方式将非自治系统转化为自治系统.其次,利用摄动法和双傅里叶级数展开的方法求得了系统的矩Lyapunov指数与最大Lyapunov指数的近似解析结果,并和利用Monte Carlo仿真得到的数值结果进行了比较验证.最后,通过对系统矩Lyapunov指数和最大Lyapunov指数解析结果的研究分析,分别讨论了次谐共振和组合共振对系统随机稳定性的影响.     

即熟型3D食品打印机的设计

针对现有3D食品打印机打印后无法直接烤熟食用的不足,设计了一种恒温加热、恒压匀速出料、快速烤熟的即熟型3D食品打印机.采用虚拟制造技术,通过SolidWorks软件设计3D食品打印机的机械系统,并基于PC机设计了3D食品打印机的测控系统.采用气压传感器实时检测物料瓶内气压,并实现匀速出料控制;采用PT100温度传感器检测烤盘表面温度,实现恒温加热控制;通过有限元分析及实验研究,给出烤盘合适的加热温度和打印轨迹.实验表明,所设计的即熟型3D食品打印机可以高效、高品质地实现食品的3D打印和烤熟,具有较好的实用价值.     

High Efficiency Perovskite Solar Cells Exceeding 22% via a Photo-Assisted Two-Step Sequential Deposition

One of the most effective methods to achieve high-performance perovskite solar cells (PSCs) is to employ additives as crystallization agents or to passivate defects. Tri-iodide ion has been known as an efficient additive to improve the crystallinity, grain size, and morphology of perovskite films. However, the generation and control of this tri-iodide ion are challenging. Herein, an efficient method to produce tri-iodide ion in a precursor solution using a photoassisted process for application in PSCs is developed. Results suggest that the tri-iodide ion can be synthesized rapidly when formamidinium iodide (FAI) dissolved isopropyl alcohol (IPA) solution is exposed to LED light. Specifically, the photoassisted FAI–IPA solution facilitates the formation of fine perovskite films with high crystallinity, large grain size, and low trap density, thereby improving the device performance up to 22%. This study demonstrates that the photoassisted process in FAI dissolved IPA solution can be an alternative strategy to fabricate highly efficient PSCs with significantly reduced processing times.     

Synergistic Effects of Cation and Anion in an Ionic Imidazolium Tetrafluoroborate Additive for Improving the Efficiency and Stability of Half-Mixed Pb-Sn Perovskite Solar Cells

Narrow-bandgap mixed Pb-Sn perovskite solar cells (PSCs) have great feasibility for constructing efficient all-perovskite tandem solar cells, in combination with wide-bandgap lead halide PSCs. However, the power conversion efficiency of mixed Pb-Sn PSCs still lags behind lead-based counterparts. Here, additive engineering using ionic imidazolium tetrafluoroborate (IMBF₄) is proposed, where the imidazolium (IM) cation and tetrafluoroborate (BF₄) anion efficiently passivate defects at grain boundaries and improve crystallinity, simultaneously relaxing lattice strain, respectively. Defect passivation is achieved by the chemical interaction between the IM cation and the positively charged under-coordinated Pb²⁺ or Sn²⁺ ions, and lattice strain relaxation is realized by lattice expansion with the intercalation of BF₄ anions into the perovskite lattice. As a result, the synergistic effects of the cation and anion in the IMBF₄ additive greatly enhance the optoelectronic performance of half-mixed Pb-Sn perovskites, leading to much longer carrier lifetimes. The best-performing half-mixed Pb-Sn PSC shows an efficiency above 19% with negligible hysteresis, while retaining over 90% of its initial efficiency after 1000 h in a nitrogen-filled glovebox and showing a lifetime to 80% degradation of 53.5 h under continuous illumination.     

3D Printing and Nanotechnology: A Multiscale Alliance in Personalized Medicine

3D printing and nanotechnology have been two important tools in the development of therapeutic approaches for personalized medicine. More recently, their alliance has been improved in an effort to build innovative, versatile, multifunctional, and/or smart medical and pharmaceutical products. Therefore, an extensive review about scientific studies that ally 3D printing and nanomaterials in the development of new approaches for pharmaceutical and medical applications for the treatment and prevention of diseases is presented here. The articles are classified into five categories according to their main application: Cell growth and tissue engineering, antimicrobial, drug delivery, stimulus-response, and theranostics. Semisolid extrusion, inorganic nanoparticles, and cell growth and tissue engineering are the most reported 3D printing technique, type of nanomaterial, and application, respectively. The increase in papers dedicated to these areas is also notable, especially in the 2019 and 2020, when semisolid extrusion became the most used technique, overcoming fused deposition modelling. In fact, this review highlights that the possibility of an alliance between 3D printing and nanotechnology for the production of multiscale materials is undoubtedly a great opportunity for knowledge and innovation in the pharmaceutical and medical area.     

Dual Additive for Simultaneous Improvement of Photovoltaic Performance and Stability of Perovskite Solar Cell

Additive engineering is one of the most efficient approaches to improve not only photovoltaic performance but also phase stability of formamidinium (FA)-based perovskite. Chlorine-based additives, such as methylammonium chloride (MACl), have been in general used to improve phase stability of FAPbI₃, which however often leads to loss of open-circuit voltage V_oc, accompanied by instability of the perovskite phase due to the volatile nature of the MA cation. A dual additive strategy for improving V_oc and thereby the overall efficiency are reported here. The mixing ratio of MACl to CsCl is varied from [MACl]/[CsCl] = 4 to 1, where V_oc increases with decreasing the ratio and best performance is achieved from [MACl]/[CsCl] = 2. As compared to the single source of MACl, the addition of CsCl reduces trap density and increases resistance against charge recombination, which is responsible for the increased V_oc. Moreover, defect passivation achieved by dual additive enables better stability than the single additive MACl as confirmed by long-term stability tests with unencapsulated devices for 50 days under relative humidity of about 40% at room temperature. The best power conversion efficiency of 23.22% is achieved by dual additive, which is higher than that for single additive of MACl or CsCl.