Corrosion Study of Lead-Free Solders Exposed to Artificial Sweat
Bibliographic record
Abstract
ABSTRACT The functionality of today's smart phones has expanded beyond the mobile phone to include e-mail, calendar, contacts, text messaging, photography, music, web surfing, gaming, etc. The smart phone's use has become pervasive in recent years since Facebook, You Tube and Twitter became popular. People are using them everywhere including the gym during their workouts. Under these circumstances, sweat can enter the phone through one of several openings – the USB port, the microphone, the headset port or the keypad and may corrode the metal solder joints inside the phone, rendering it inoperable. Thus, the corrosion of lead-free solders through exposure to sweat is a topic of interest for the mobile electronics industry. The intent of this research was to investigate the corrosion behavior and shear strength of various lead free solder alloys after exposure to different artificial sweat solutions. The alloys tested – Sn-3.0Ag-0.5Cu, Sn-0.7Cu-0.05Ni+Ge, Sn-3.5Ag-1.0Cu-3.0Bi and Sn-1.0Ag-0.7Cu-1.6Bi-0.2In – were investigated using three artificial sweat solutions with varying chloride concentrations and acidity. The surface morphology and corrosion product compositions were determined using optical and SEM/EDX analysis. The effect of corrosion on the mechanical properties of these alloys before and after exposure to the sweat was measured using shear strength tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".