TY - JOUR A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment BT - influences of the experimental validation setup JF - Energies N2 - In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro–thermal model is developed and experimentally validated to predict the cell’s temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell’s experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell’s thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile. UR - https://doi.org/10.3390/en13010062 KW - Lithium-Ionen-Batterie KW - Elektrofahrzeug Y1 - 2019 UR - https://doi.org/10.3390/en13010062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-7532 SN - 1996-1073 VL - 13 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Liu, E A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Signorini, Raffaella A1 - Pedron, Danilo A1 - Wunderle, Bernhard A1 - Elger, Gordon T1 - Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy JF - Journal of Raman Spectroscopy N2 - Local thermomechanical stress can cause failures in semiconductor packages during long-time operation under harsh environmental conditions. This study helps to explain the packaging-induced stress in blue GaN-LEDs soldered onto copper substrates using AuSn alloy as lead-free interconnect material. Based on the finite element method, a virtual prototype is developed to simulate the thermomechanical behavior and stress in the LED and in the complete LED/AuSn/Cu assembly considering plastic and viscoplastic strain. The investigations were performed by varying the temperature between −50°C and 180°C. To validate the model, the simulation results are compared to experimental data collected with Raman spectroscopy. Studies of the urn:x-wiley:03770486:media:jrs5947:jrs5947-math-0003 phonon mode of GaN semiconductor are elaborated to understand the induced thermomechanical stress. The model enables evaluation of the stress in the interfaces of the assembly, which otherwise cannot be accessed by measurements. It serves to predict how assemblies would perform, before committing resources to build a physical prototype. UR - https://doi.org/10.1002/jrs.5947 KW - finite element method KW - gallium nitride (GaN) KW - LED KW - semiconductor KW - thermomechanical stress Y1 - 2020 UR - https://doi.org/10.1002/jrs.5947 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9438 SN - 1097-4555 VL - 51 IS - 10 SP - 2083 EP - 2094 PB - Wiley CY - Chichester ER - TY - JOUR A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Liu, E A1 - Elger, Gordon T1 - Comparison of Nondestructive Testing Methods for Solder, Sinter, and Adhesive Interconnects in Power and Opto-Electronics JF - Applied Sciences N2 - Reliability is one of the major requirements for power and opto-electronic devices across all segments. High operation temperature and/or high thermomechanical stress cause defects and degradation of materials and interconnects, which may lead to malfunctions with costly or even life-threatening consequences. To avoid or at least reduce failures, nondestructive testing (NDT) methods are common within development and production of power and opto-electronics. Currently, the dominating NDT methods are X-ray, scanning acoustic microscopy (SAM), and transient thermal analysis (TTA). However, they have different strengths and weaknesses with respect to materials and mechanical designs. This paper compares these NDT methods for different interconnect technologies, i.e., reflow soldering, adhesive, and sintered interconnection. While X-ray provided adequate results for soldered interfaces, inspection of adhesives and sintered interconnects was not possible. With SAM, evaluation of adhesives and sintered interconnects was also feasible, but quality depended strongly on the sample under test. TTA enabled sufficiently detailed results for all the interconnect applications. Automated TTA equipment, as the in-house developed tester used within this investigation, enabled measurement times compatible with SAM and X-ray. In the investigations, all methods revealed their pros and cons, and their selection has to depend on the sample under tests and the required analysis depth and data details. In the paper, guidelines are formulated for an appropriate decision on the NDT method depending on sample and requirements. UR - https://doi.org/10.3390/app10238516 KW - reliability KW - nondestructive testing KW - power electronics KW - X-ray KW - scanning acoustic microscopy KW - transient thermal analysis KW - TTA KW - sintering KW - LED KW - MOSFET Y1 - 2020 UR - https://doi.org/10.3390/app10238516 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12518 SN - 2076-3417 VL - 10 IS - 23 PB - MDPI CY - Basel ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection JF - Sensors N2 - Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output. UR - https://doi.org/10.3390/s25113422 Y1 - 2025 UR - https://doi.org/10.3390/s25113422 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59421 SN - 1424-8220 VL - 25 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Da Rosa Zanatta, Mateus A1 - Carvalho Lustosa da Costa, Joao Paulo A1 - Antreich, Felix A1 - Haardt, Martin A1 - Elger, Gordon A1 - Lopes de Mendonça, Fábio Lúcio A1 - de Sousa Junior, Rafael Timóteo T1 - Tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios JF - IEEE Access N2 - Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe). UR - https://doi.org/10.1109/ACCESS.2020.3024597 KW - Global navigation satellite systems (GNSS) KW - global positioning system (GPS) KW - GPS3 KW - time-delay estimation (TDE) KW - multipath components KW - model order selection (MOS) Y1 - 2020 UR - https://doi.org/10.1109/ACCESS.2020.3024597 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9528 SN - 2169-3536 VL - 8 SP - 174931 EP - 174942 PB - IEEE CY - New York ER - TY - JOUR A1 - Mohan, Nihesh A1 - Ahuir-Torres, Juan Ignacio A1 - Kotadia, Hiren R. A1 - Elger, Gordon T1 - Laser sintering of Cu particle-free inks for high-performance printed electronics JF - npj Flexible Electronics N2 - This study investigates laser sintering of Cu particle-free ink (Cu formate tetrahydrate—amino-2-propanol complex) as an alternative to conventional sintering in an oven (under inert/reducing atmosphere). Utilizing benefits of high-speed localized heating using laser, substrate damage can be prevented for low-melting substrates such as Polyethylene Terephthalate (PET). Firstly, a suitable sintering process window is achieved based on energy density for two different flexible polymeric susbtrates: Polyimide and PET using different laser parameters (laser power, scan rate and spot diameter). Subsequently, characterization of laser sintered traces are also made using different laser optic profiles (Gaussian and top hat). Different methodologies for fabrication of metallized Cu layer were also demonstrated. A very low bulk resistivity of 3.24 µΩcm (1.87 times of bulk Cu) was achieved on trace thickness of 0.85 ± 0.15 µm exhibiting good adherence to polymeric substrates. A promising fabrication process of low-cost and reliable flexible printed electronic devices is demonstrated. UR - https://doi.org/10.1038/s41528-025-00389-5 Y1 - 2025 UR - https://doi.org/10.1038/s41528-025-00389-5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58266 SN - 2397-4621 VL - 9 IS - 1 PB - Springer CY - London ER - TY - JOUR A1 - Theissler, Andreas A1 - Pérez-Velázquez, Judith A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry JF - Reliability Engineering & System Safety N2 - Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data. UR - https://doi.org/10.1016/j.ress.2021.107864 KW - predictive maintenance KW - artificial intelligence KW - machine learning KW - deep learning KW - vehicle KW - automotive KW - reliability KW - lifetime prediction KW - condition monitoring Y1 - 2021 UR - https://doi.org/10.1016/j.ress.2021.107864 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9673 SN - 0951-8320 VL - 2021 IS - 215 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Agrawal, Shiva A1 - Bhanderi, Savankumar A1 - Elger, Gordon T1 - Semi-Automatic Annotation of 3D Radar and Camera for Smart Infrastructure-Based Perception JF - IEEE Access N2 - Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/. UR - https://doi.org/10.1109/ACCESS.2024.3373310 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3373310 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46180 SN - 2169-3536 VL - 12 SP - 34325 EP - 34341 PB - IEEE CY - New York ER - TY - JOUR A1 - Mohan, Nihesh A1 - Steinberger, Fabian A1 - Wächter, Sonja A1 - Erdogan, Hüseyin A1 - Elger, Gordon T1 - Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes JF - Applied Sciences N2 - Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper–polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (<150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper. UR - https://doi.org/10.3390/app15052676 Y1 - 2025 UR - https://doi.org/10.3390/app15052676 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58065 SN - 2076-3417 VL - 15 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Saccon, Rodolfo A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Formic acid and formate salts for chemical vapor deposition of copper on glass substrates at atmospheric pressure JF - New Journal of Chemistry UR - https://doi.org/10.1039/D1NJ02476K Y1 - 2021 UR - https://doi.org/10.1039/D1NJ02476K SN - 1369-9261 VL - 45 IS - 43 SP - 20133 EP - 20139 PB - RSC CY - London ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Kotadia, Hiren R. A1 - Conti, Fosca A1 - Mauser, Armin A1 - Rubenbauer, Thomas A1 - Bruetting, Robert A1 - Schneider-Ramelow, Martin A1 - Elger, Gordon T1 - Die-attach bonding with etched micro brass metal pigment flakes for high-power electronics packaging JF - ACS Applied Electronic Materials UR - https://doi.org/10.1021/acsaelm.1c00721 KW - sintering KW - brass flakes KW - nanostructured materials KW - self-reducing KW - etching KW - surface modifications Y1 - 2021 UR - https://doi.org/10.1021/acsaelm.1c00721 SN - 2637-6113 VL - 3 IS - 10 SP - 4587 EP - 4603 PB - ACS Publications CY - Washington ER - TY - JOUR A1 - Kleiner, Jan A1 - Singh, Randeep A1 - Schmid, Michael A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Influence of heat pipe assisted terminal cooling on the thermal behavior of a large prismatic lithium-ion cell during fast charging in electric vehicles JF - Applied Thermal Engineering UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Fast charging KW - Terminal cooling KW - Heat pipe Y1 - 2020 UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 SN - 1359-4311 VL - 2021 IS - 188 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Maximilian A1 - Bhogaraju, Sri Krishna A1 - Hanss, Alexander A1 - Elger, Gordon T1 - A new noise-suppression algorithm for transient thermal analysis in semiconductors over pulse superposition JF - IEEE Transactions on Instrumentation and Measurement UR - https://doi.org/10.1109/TIM.2020.3011818 KW - LED KW - linear time-invariant (LTI) system KW - MOSFET KW - noise suppression KW - reliability KW - semiconductor KW - signal processing KW - thermal impedance (Zth) KW - transient thermal analysis (TTA) Y1 - 2021 UR - https://doi.org/10.1109/TIM.2020.3011818 SN - 0018-9456 VL - 70 PB - IEEE CY - New York ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Elger, Gordon T1 - Die-attach bonding for high temperature applications using thermal decomposition of copper(II) formate with polyethylene glycol JF - Scripta Materialia UR - https://doi.org/10.1016/j.scriptamat.2020.02.045 Y1 - 2020 UR - https://doi.org/10.1016/j.scriptamat.2020.02.045 SN - 1359-6462 VL - 2020 IS - 182 SP - 74 EP - 80 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Bhogaraju, Sri Krishna A1 - Conti, Fosca A1 - Kotadia, Hiren R. A1 - Keim, Simon A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - Novel approach to copper sintering using surface enhanced brass micro flakes for microelectronics packaging JF - Journal of Alloys and Compounds UR - https://doi.org/10.1016/j.jallcom.2020.156043 KW - Metals and alloys KW - Microstructure KW - Surfaces and interfaces KW - Sintering Y1 - 2020 UR - https://doi.org/10.1016/j.jallcom.2020.156043 SN - 1873-4669 VL - 2020 IS - 844 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mokhtari, Omid A1 - Conti, Fosca A1 - Bhogaraju, Sri Krishna A1 - Meier, Markus A1 - Schweigart, Helmut A1 - Tetzlaff, Ulrich A1 - Elger, Gordon T1 - Characterization of tin-oxides and tin-formate crystals obtained from SnAgCu solder alloy under formic acid vapor JF - New Journal of Chemistry UR - https://doi.org/10.1039/C9NJ02135C Y1 - 2019 UR - https://doi.org/10.1039/C9NJ02135C SN - 1369-9261 VL - 43 IS - 26 SP - 10227 EP - 10231 PB - RSC CY - London ER - TY - JOUR A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Pérez-Velázquez, Judith A1 - Elger, Gordon T1 - Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks JF - Microelectronics Reliability N2 - The thermo-mechanical fatigue of different SAC+ solders is investigated using transient thermal analysis (TTA) and predicted using artificial neural networks (ANN). TTA measures the thermal impedance and allows detection of solder cracks and delamination of material interfaces. LEDs soldered to printed circuit boards using seven different solders were aged within passive air-to-air temperature shock tests with TTA measurements every 50 cycles with the increase of the thermal resistance as failure criterium. A SnAgCuSb solder showed the best performance improvement over the SAC305 reference under the test conditions. In addition to standard evaluation by the cumulative failure-curve and Weibull plot, new approaches for reliability assessment are investigated to assess the reliability of the solder joint of the individual LEDs. A hybrid approach to predict failures in the solder joints of the individual LEDs during accelerated stress testing is set-up which processes the TTA data using artificial neural networks with memory, specifically LSTM, where the memory allows full use of the measurement history. Two ANN approaches, regression and classification, are used. Both approaches are shown to be quite accurate. The greater information gained from the regression approach requires more processing using external knowledge of the problem requirements, whereas the categorical approach can be more directly implemented. The results demonstrate the advantages of integrated approaches for assessment of the remaining useful life of solder joints. UR - https://doi.org/10.1016/j.microrel.2021.114461 KW - Artificial neural networks KW - LSTM KW - Prediction KW - Reliability KW - Solder joints KW - Transient thermal analysis Y1 - 2022 UR - https://doi.org/10.1016/j.microrel.2021.114461 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-19719 SN - 0026-2714 VL - 2022 IS - 129 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Liu, E A1 - Elger, Gordon T1 - Transient thermal analysis as measurement method for IC package structural integrity JF - Chinese Physics B UR - https://doi.org/10.1088/1674-1056/24/6/068105 Y1 - 2015 UR - https://doi.org/10.1088/1674-1056/24/6/068105 SN - 2058-3834 SN - 1674-1056 VL - 24 IS - 6 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Hanss, Alexander A1 - Elger, Gordon T1 - Residual free solder process for fluxless solder pastes JF - Soldering & Surface Mount Technology UR - https://doi.org/10.1108/SSMT-10-2017-0030 KW - Reflow soldering KW - LED KW - Fluxless KW - Solder process Y1 - 2018 UR - https://doi.org/10.1108/SSMT-10-2017-0030 SN - 0954-0911 VL - 30 IS - 2 SP - 118 EP - 128 PB - Emerald CY - Bingley ER - TY - JOUR A1 - Conti, Fosca A1 - Hanss, Alexander A1 - Mokhtari, Omid A1 - Bhogaraju, Sri Krishna A1 - Elger, Gordon T1 - Formation of tin-based crystals from a SnAgCu alloy under formic acid vapor JF - New Journal of Chemistry UR - https://doi.org/10.1039/C8NJ04173C Y1 - 2018 UR - https://doi.org/10.1039/C8NJ04173C SN - 1369-9261 VL - 42 IS - 23 SP - 19232 EP - 19236 PB - RSC CY - London ER -