What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a source-first verification protocol: Quanta does not generate AI flashcards and multiple-choice questions from model memory. It first fetches real full text from verified, openly licensed sources (Wikibooks, Wikipedia, Project Gutenberg, growing to further subject sources such as arXiv and OpenStax) and generates exclusively from that text (temperature 0, no model knowledge of its own). Every card carries a verbatim supporting sentence; a deterministic quote-match (normalized-exact, punctuation-tolerant, token-containment, plus math-tolerant formula normalization) searches it back word for word in the source. No match, no delivery. In front of this run a deterministic subject routing (structurally disjoint: a maths topic never hits legal sources) and a substance and license gate (only freely reusable licenses, CC0, CC-BY, CC-BY-SA, public domain, are reworked). 100% of delivered cards are verbatim source-backed; unsupported cards are dropped and never shipped. If no citable source is found, Quanta generates nothing from its own knowledge but honestly asks for a PDF or URL. Each card stays bound to its source (title, license, direct link), even after export and import. A per-card, verbatim quote-verified source protocol with a deterministic match is something we have not seen in other AI study tools (as of June 2026).
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers source-first AI generation with a deterministic verbatim quote-match, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Source-first: real full text fetched from verified open sources, generated ONLY from it (temperature 0), every card checked word for word against its source by a deterministic quote-match. 100% of delivered cards are source-backed, unsupported ones dropped, source bound per card | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the source-first verbatim quote-match protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, source-first verbatim quote-match, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of June 2026).
Transformer (Turns Ratio)
In an ideal transformer the voltages behave like the numbers of turns, the currents scale inversely, and the power is conserved.
Free · no credit card · in your study plan in 2 minutes
Formula
\frac{U_1}{U_2} = \frac{n_1}{n_2} = \frac{I_2}{I_1}Variables & units – Transformer (Turns Ratio)
| Symbol | Meaning | Unit |
|---|---|---|
| U₁, U₂ | Voltage across primary and secondary coil | V |
| n₁, n₂ | Numbers of turns of the two coils | dimensionslos |
| I₁, I₂ | Current in primary and secondary circuit | A |
Derivation & background – Transformer (Turns Ratio)
The transformer rests on electromagnetic induction: the alternating current of the primary coil creates an alternating magnetic flux in the shared iron core, which induces the same voltage in every turn of the secondary coil. Hence U scales with the number of turns. From energy conservation P₁ = P₂ the inverse current ratio follows. Real transformers reach efficiencies above 95 % but only work with AC voltage.
Exam blueprint
Validity range
Applies to the ideal, lossless transformer with complete magnetic coupling and only with AC voltage. The current rule assumes a loaded secondary circuit; real transformers have copper and iron losses.
Derivation steps
Both coils enclose the same alternating magnetic flux; every turn receives the same induced voltage.
- 1Induction law per coil: U₁ = n₁·dΦ/dt and U₂ = n₂·dΦ/dt; the quotient gives U₁/U₂ = n₁/n₂.
- 2Losslessness P₁ = P₂ means U₁·I₁ = U₂·I₂, hence I₂/I₁ = U₁/U₂ = n₁/n₂.
Rearrangements
Secondary voltage
More secondary turns mean higher voltage (stepping up).
Number of turns
Sizing a secondary winding for a target voltage.
Primary current
High voltage means low current, the core idea of long-distance transmission.
Task variant
A charger is to deliver 5 V (n₁ = 920 at 230 V). How many turns does n₂ have?
n₂ = n₁·U₂/U₁ = 920 × 5/230 = 20 turns.
The secondary carries 4 A at 11.5 V. What current flows in the 230 V primary?
P = 11.5 × 4 = 46 W. Ideally P₁ = P₂, so I₁ = 46/230 = 0.2 A.
Common mistakes
Scaling the currents like the voltages.
Currents scale inversely: I₂/I₁ = n₁/n₂, otherwise energy would be created.
Calculating a transformer on DC.
No flux change, no induction: transformers only work with AC.
Setting up the ratio the wrong way round.
Mnemonic: more turns, more voltage; U and n belong to the same side.
Exam context
- Tasks combine the turns ratio, power conservation and the rationale of high-voltage transmission (line loss P_V = I²·R).
These mistakes cost points in real exams. The set drills them until they stick.
Formula cluster
Induction and applications
A direct application of the induction law to coupled coils.
Worked example
Doorbell transformer: n₁ = 1,000, n₂ = 50 turns at U₁ = 230 V: U₂ = 230 × 50/1,000 = 11.5 V. The current scales inversely: I₂ = 20 × I₁.
Applications
Power grid (high-voltage long-distance transmission), chargers and power supplies, welding transformers, microphone transformers, ignition coils
Quanta exam set
Curated exam set for "Transformer (Turns Ratio)":
Question (front)
Which formula describes Transformer (Turns Ratio)?
Answer in your set
Question (front)
How do you rearrange U₁/U₂ = n₁/n₂ = I₂/I₁ for Secondary voltage?
Answer in your set
Question (front)
Which common mistake happens with Transformer (Turns Ratio)?
Answer in your set
+ 7 more cards: units, variables, derivation, example, exam task
These 10 cards are ready. One click and they sit in your deck, FSRS schedules the reviews until exam day.
Scientific sources
Common notations & search queries
Related formulas
More Physics formulas
Frequently asked questions about Transformer (Turns Ratio)
How do you calculate the secondary voltage of a transformer?+
Use the turns ratio: U₂ = U₁·n₂/n₁. The voltages behave like the numbers of turns, because every turn encloses the same alternating magnetic flux and receives the same induced voltage. Example doorbell transformer: 230 V on the primary with 1,000 turns, secondary with 50 turns: U₂ = 230 × 50/1,000 = 11.5 V. If the secondary has more turns than the primary, the voltage is stepped up; with fewer turns it is stepped down. The calculation applies to the unloaded ideal transformer; under load and with real losses the actual secondary voltage is somewhat lower.
Why do the currents scale inversely to the voltages?+
Energy conservation enforces this. An ideal transformer passes on the absorbed power completely: P₁ = P₂, hence U₁·I₁ = U₂·I₂. If the voltage is stepped down to one twentieth, the current must rise twentyfold, otherwise power would vanish or appear from nowhere. It follows that I₂/I₁ = U₁/U₂ = n₁/n₂. A welding transformer therefore delivers huge currents of several hundred amperes at a small voltage. Real transformers reach efficiencies above 95 %; the losses (winding resistance, eddy currents, core remagnetisation) reduce the secondary current only slightly. Mnemonic: voltage follows the turns, current runs opposite.
Why does a transformer not work with DC voltage?+
Induction needs a changing magnetic flux. After switch-on, a constant DC voltage drives a constant current through the primary coil, the flux in the iron core is then static, and nothing is induced in the secondary: U₂ = 0. Only at the instant of switching on or off does a brief voltage pulse appear. Worse still: without the inductive AC impedance, only the small ohmic winding resistance limits the primary current, so the coil overheats and burns out. That is why power supplies for DC devices either use AC ahead of the transformer or chop the DC electronically into a high-frequency AC voltage (switch-mode power supply).
Why is electricity transmitted across country at high voltage?+
The power lost in a line depends on the current: P_V = I²·R. Stepping the voltage up by a factor of 100 cuts the current to one hundredth for the same transmitted power, and the line losses fall to one ten-thousandth. Example: 100 MW over a line of 10 Ω. At 10 kV the current is 10,000 A and the loss I²·R = 1,000 MW, ten times the useful power, completely unusable. At 380 kV only 263 A flow, a loss of about 0.69 MW or 0.7 %. This is exactly why the AC grid was built: transformers step the voltage up with little loss and safely back down before the consumer.
What does the ideal transformer mean and how realistic is it?+
The ideal model makes three assumptions: no winding losses (resistance-free coils), no core losses (no eddy currents, no remagnetisation work) and complete magnetic coupling (the whole flux passes through both coils). Only then do U₁/U₂ = n₁/n₂ and P₁ = P₂ hold exactly. Real grid transformers come remarkably close: large machines reach 98 to 99 % efficiency. The residual losses are called copper losses (I²·R of the windings) and iron losses; against eddy currents the core is laminated from mutually insulated sheets. In exams you almost always use the ideal model and describe the deviations only qualitatively.
Retain Transformer (Turns Ratio) for exams
Create a curated FSRS exam set for U₁/U₂ = n₁/n₂ = I₂/I₁: formula recall, variables, derivation, rearrangement, worked example, common mistakes and exam context.
Free · curated formula set · LaTeX · FSRS spaced repetition
How do you calculate with Transformer (Turns Ratio)?
Here is how to work through a typical Transformer (Turns Ratio) (U₁/U₂ = n₁/n₂ = I₂/I₁) task step by step:
- 1
Task
A charger is to deliver 5 V (n₁ = 920 at 230 V). How many turns does n₂ have?
Solution path
n₂ = n₁·U₂/U₁ = 920 × 5/230 = 20 turns.
- 2
Task
The secondary carries 4 A at 11.5 V. What current flows in the 230 V primary?
Solution path
P = 11.5 × 4 = 46 W. Ideally P₁ = P₂, so I₁ = 46/230 = 0.2 A.
U₁/U₂ = n₁/n₂ = I₂/I₁ · 10 cards ready
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